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Behold the Intrinsic Value of IP

Monday, March 13th, 2017

By Grant Pierce, CEO

Sonics, Inc.

Editor’s Note [this article was written in response to questions about IP licensing practices.  A follow-up article will be published in the next 24 hours with the title :” Determining a Fair Royalty Value for IP”].


Understanding the intrinsic value of Intellectual Property is like beauty, it is in the eye of the beholder.  The beholder of IP Value is ultimately the user/consumer of that IP – the buyers. Buyers tend to value IP based upon their ability to utilize that IP to create competitive advantage, and therefore higher value for their end product. The IP Value figure above was created to capture this concept.

To be clear, this view is NOT about relative bargaining power between buyer and the supplier of IP – the seller –  that is built on the basis of patents. Mounds of court cases and text books exist that explore the question of patent strength. What I am positing is that viewing IP value as a matter of a buyer’s perception is a useful way to think of the intrinsic value of IP.

Position A on the value chart is a classification of IP that allows little differentiation by the buyer, but is addressing a more elastic market opportunity. This would likely be a Standard IP type that would implement an open standard. IP in this category would likely have multiple sources and therefore competitive pricing.  Although compliance with the standard would be valued by the buyer, the price of the IP itself would be likely lower reflecting its commodity nature. Here, the value might be equated to the cost of internally creating equivalent IP. Since few, if any, buyers in this category would see advantage for making this IP themselves and because there are likely many sellers, the intrinsic value of this IP is determined on a “buy vs buy” basis.  Buyers are going to buy this IP regardless, so they’ll look for the seller with the proposition most favorable to the buyer – which often is just about price.

Position B on the value chart is a classification of IP that allows for differentiation by the buyer, but addresses a more elastic market. IP in this category might be less constrained by standards requirements. It is likely that buyers would implement unique instantiations of this IP type and as a result command some end competitive advantage. Buyers in this category could make this IP themselves, but because there are commercial alternatives, the intrinsic value is determined by applying a “make vs buy” analysis. The value proposition of the sellers of this type of IP often include some important, but soft value propositions (e.g., ease of re-use, time-to-market, esoteric features), the make vs buy determination is highly variable and often buyer-specific. This in part explains the variability of pricing for this type of IP.

Position C on the value chart is a classification of IP that serves a less elastic market and empowers buyers to differentiate through their unique implementations of that IP. This classification of IP supports license fees and larger, more consistent, royalty rates. IP in this category becomes the competitive differentiation that sways large market share to the winning products incorporating that IP. This category supports some of the larger IP companies in the marketplace today. Buyers in this category are not going to make the IP themselves because the cost of development of the product and its ecosystem is too prohibitive and risky. The intrinsic value really comes down to what the seller charges.

This is a “buy vs not make” decision – meaning one either buys the IP or it doesn’t bother to make the product. A unique hallmark of IP in this position is that so long as the seller applies pricing consistently, then all buyers know at the very least that they are not disadvantaged relative to the competition and will continue to buy. Sellers will often give some technology away to encourage long-term lock in. For these reasons, pricing of IP in this space tends to be quite stable. That pricing level must subjectively be below the level that customers begin to perform unnatural acts and explore unusual alternatives.  So long as it does, the price charged probably represents accurately the intrinsic value.

Position D on the value chart is a classification of IP that requires adherence to a standard. Like category A, adherence to the standard does not necessarily allow differentiation to the buyer. The buyer of this category of IP might be required to use this IP in order to gain access to the market itself. Though the lack of end-product differentiation available to the buyer might suggest a lower license fee and/or lower to zero royalty rate, we see a significantly less elastic market for this IP type.

This IP category tends to comprise products adhering to closed and/or proprietary standards. IP products built on such closed and/or proprietary standards have given rise to several significant IP business franchises in the marketplace today. The IP in position D is in part characterized by the need to spend significant time and money to develop, market and maintain (defend) their position, in addition to spending on IP development. For this reason, teasing out the intrinsic value of this IP is not as straightforward as “make vs buy.” Pricing is really viewed more as a tax. So the intrinsic value determination is based on a “Fair Tax” basis. If buyers think the tax is no longer “fair,” for any reason, they will make the move to a different technology.


Position A:  USB, PCI, memory interfaces (Synopsys)

Position B:  Configurable Processors, Analog IP cores (Synopsys, Cadence)

Position C:  General Purpose Processors, Graphics, DSP, NoC, EPU (ARM, Imagination, CEVA, Sonics)

Position D: CDMA, Noise Reduction, DDR (Qualcomm, Dolby, Rambus)

Why Customer Success is Paramount

Sonics is an IP supplier whose products tend to reside in the Type C category. Sonics sets its semiconductor IP pricing as a function of the value of the SoC design/chip that uses the IP. There is a spectrum of value functions for the Sonics IP depending upon the type of chip, complexity of design, target power/performance, expected volume, and other factors. Defining the upper and lower bounds of the value spectrum depends upon an approximation of these factors for each particular chip design and customer.

Royalties are one component of the price of IP and are a way of risk sharing to allow customers to bring their products to market without having to pay the full value of the incorporated IP up front. The benefit being that the creator and supplier of the IP is essentially investing in the overall success of the user’s product by accepting the deferred royalty payment. Sonics views the royalty component of its IP pricing as “customer success fees.”

With its recently introduced EPU technology, Sonics has adopted an IP business model based upon an annual technology access fee and a per power grain usage fee due at chip tapeout. Under this model, customers have unlimited use of the technology to explore power control for as many designs as they want, but only pay for their actual IP usage in a completed design. The tape out fee is calculated based on the number of power grains used in the design on a sliding scale. The more power grains customers use, the more energy saved, and the lower the cost per grain. Using more power grains drives lower energy consumption by the chip – buyers increase the market value of their chips using Sonics’ EPU technology. The bottom line is that Sonics’ IP business model depends on customers successfully completing their designs using Sonics IP.

Low Power Is The Norm, Not The Exception

Friday, September 26th, 2014

Gabe Moretti, Senior Editor

The issue of power consumption took front stage with the introduction of portable electronic devices.  It became necessary for the semiconductor industry and thus the EDA industry to develop new methods and new tools to confront the challenges and provide solutions.  Thus Low Power became a separate segment of the industry.  EDA vendors developed tools specifically addressing the problem of minimizing power consumption, both at the architecture, the synthesis, and the pre-fabrication stage of IC development.  Companies instituted new design methodologies that focused specifically on power distribution and consumption.

Today the majority of devices are designed and fabricated with low power as a major requirement.  As we progress toward a world that uses more wearable devices and more remote computational capabilities, low power consumption is a must.  I am not sure that dedicating a segment to low power is relevant: it makes more sense to have a sector of the industry devoted to unrestricted power use instead.

The contributions I received in preparing this article are explicit in supporting this point of view.

General Considerations

Mary Ann White, Director of Product Marketing, Galaxy Design Platform, at Synopsys concurs with my position.  She says: “Power conservation occurs everywhere, whether in mobile applications, servers or even plug-in-the-wall items.  With green initiatives and the ever-increasing cost of power, the ability to save power for any application has become very important.  In real-world applications for home consumer items (e.g. stereo equipment, set-top boxes, TVs, etc.), it used to be okay to have items go into standby mode. But, that is no longer enough when smart-plug strips that use sensors to automatically turn off any power being supplied after a period of non-usage are now populating many homes and Smart Grids are being deployed by utility companies. This trend follows what commercial companies have done for many years now, namely using motion sensors for efficient energy management throughout the day.”

Vic Kulkarni, Senior VP and GM, RTL Power Business Unit, at Apache Design, Inc., a wholly-owned subsidiary of ANSYS, Inc. approached the problem from a different point of view but also points out wasted power.

“Dynamic power consumed by SoCs continues to rise in spite of strides made in reducing the static power consumption in advanced technology nodes.

There are many reasons for dynamic power consumption waste – redundant data signal activity when clocks are shut off, excessive margin in the library characterization data leading to inefficient implementation, large active logic cones feeding deselected mux inputs, lack of sleep or standby mode for analog circuits, and even insufficient software-driven controls to shut down portions of the design. Another aspect is the memory sub-system organization. Once the amount of memory required is known, how should it be partitioned? What types of memories should be used? How often do they need to be accessed? All of these issues greatly affect power consumption. Therefore, design must perform power-performance-area tradeoffs for various alternative architectures to make an informed decision.”

The ubiquity of low power designs was also pointed out by Guillaume Boillet, Technical Marketing Manager, at Atrenta Inc.  He told me that: “Motivations for reducing the power consumed by chips are multiple. They range from purely technical considerations (i.e. ensuring integrity and longevity of the product), to differentiation factors (i.e. extend battery life or reduce cost of cooling) to simply being more socially responsible. As a result, power management techniques, which were once only deployed for wireless applications, have now become ubiquitous. The vast majority of IC designers are now making a conscious effort to configure their RTL for efficient power partitioning and to reduce power consumption, in particular the dynamic component, which is increasingly becoming more dominant at advanced technology nodes.”  Of course experience by engineers has found that minimizing power is not easy.”  Guillaume continued: “The task is vast and far from being straight-forward. First, there is a multitude of techniques which are available to designers: Power gating, use of static and variable voltage domains, Dynamic Voltage and Frequency Scaling (DVFS), biasing, architectural tradeoffs, coarse and fine-grain clock gating, micro-architectural optimizations, memory management, and light sleep are only some examples. When you try combining all of these, you soon realize the permutations are endless. Second, those techniques cannot be applied blindly and can have serious implications during floor planning, timing convergence activities, supply distribution, Clock Tree Synthesis (CTS), Clock Domain Crossing management, Design For Test (DFT) or even software development.”

Low power considerations have also been at the forefront of IP designs.  Dr. Roddy Urquhart is Vice President of Marketing at Cortus, a licensor of controllers, noted that: “A major trend in the electronics industry now, is the emergence of connected intelligent devices implemented as systems-on-chip (SoC) – the ‘third wave’ of computational devices.  This wave consists of the use of locally connected smart sensors in vehicles, the emergence of “smart homes” and “smart buildings” and the growing Internet of Things.  The majority of these types of devices will be manufactured in large volumes, and will face stringent power constraints. While users may accept charging their smartphones on a daily basis, many sensor-based devices for industrial applications, environmental monitoring or smart metering rely on the battery to last months or even a number of years. Achieving this requires a focus on radically reducing power and a completely different design approach to the SoC design.”

Architectural Considerations

Successful power management starts at the architectural level.  Designers cannot decide on a tactic to conserve power once that system has already been designed, since power consumption is the result of architectural decisions aimed at meeting functional requirements.  These tradeoffs are made very early in the development of an IC.

Jon McDonald, Senior Technical Marketing Engineer, at Mentor Graphics noted that: “Power analysis needs to begin at the system level in order to fix a disconnect between the measurement of power and the decisions that affect power consumption. The current status quo forces architectural decisions and software development to typically occur many months before implementation-based power measurement feedback is available. We’ve been shooting in the dark too long.  The lack of visibility into the impact of decisions while they are being made incurs significant hidden costs for most hardware and software engineers. System engineers have no practical way of measuring the impact of their design decisions on the system power consumption. Accurate power information is usually not available until RTL implementation, and the bulk of power feedback is not available until the initial system prototypes are available.”

Patrick Sheridan, Senior Staff Product Marketing Manager, Solutions Group, at Synopsys went into more details.

“Typical questions that the architect can answer are:

1) How to partition the SoC application into fixed hardware accelerators and software executing on processors, determining the optimal number and type of each CPU, GPU, DSP and accelerator.

2) How to partition SoC components into a set of power domains to adjust voltage and frequency at runtime in order to save power when components are not needed.

3) How to confirm the expected performance/power curve for the optimal architecture.

To help expand industry adoption, the IEEE 1801 Working Group’s charter has been updated recently to include extending the current UPF low power specification for use in system level power modeling. A dedicated system level power sub-committee of the 1801 (UPF) Working Group has been formed, led by Synopsys, which includes good representation from system and power architects from the major platform providers. The intent is to extend the UPF language where necessary to support IP power modeling for use in energy aware system level design.”  But he pointed out that more is needed from the software developers.

“In addition, power efficiency continues to be a major product differentiator – and quality concern – for the software manager. Power management functions are distributed across firmware, operating system, and application software in a multi-layered framework, serving a wide variety of system components – from multicore CPUs to hard-disks, sensors, modems, and lights – each consuming power when activated. Bringing up and testing power management software is becoming a major bottleneck in the software development process.

Virtual prototypes for software development enable the early bring-up and test of power management software and enable power-aware software development, including the ability to:

- Quickly reveal fundamental problems such as a faulty regulation of clock and voltages

- Gain visibility for software developers, to make them aware of problems that will cause major changes in power consumption

- Simulate real world scenarios and systematically test corner cases for problems that would otherwise only be revealed in field operation

This enables software developers to understand the consequences of their software changes on power sooner, improving the user-experience and accelerating software development schedules.”

Drew Wingard, CTO, at Sonics also answered my question about the importance of architectural analysis of power consumption.

“All the research shows that the most effective place to do power optimization is at the architectural level where you can examine, at the time of design partitioning, what are the collections of components which need to be turned on or can afford to be turned off. Designers need to make power partitioning choices from a good understanding of both the architecture and the use cases they are trying to support on that architecture. They need tooling that combines the analysis models together in a way that allows them to make effective tradeoffs about partitioning versus design/verification cost versus power/energy use.”

Dr. Urquhart underscored the importance of architectural planning in the development of licensable IP.  “Most ‘third wave’ computational devices will involve a combination of sensors, wireless connectivity and digital control and data processing. Managing power will start at the system level identifying what parts of the device need to be always on or always listening and which parts can be switched off when not needed. Then individual subsystems need to be designed in a way that is power efficient.

A minimalist 32-bit core saves silicon area and in smaller geometries also helps reduce static power. In systems with more complex firmware the power consumed by memory is greater than the power in the processor core. Thus a processor core needs to have an efficient instruction set so that the size of the instruction memory is minimized. However, an overly complex instruction set would result in good code density but a large processor core. Thus overall system power efficiency depends on balancing power in the processor core and memory.”

Implementation Considerations

Although there is still a need for new and more powerful architectural tools for power planning, implementation tools that help designers deal with issues of power distribution and use are reaching maturity and can be counted as reliable tools by engineers.

Guillaume Boillet observed that: “Fine-grain sequential clock gating and removal of redundant memory accesses are techniques that are now mature enough for EDA tools to decide what modifications are best suited based on specific usage scenarios (simulation data). For these techniques, it is possible to generate optimized RTL automatically, while guaranteeing its equivalence vs. the original RTL, thanks to formal techniques. EDA tools can even prevent modifications that generate new unsynchronized crossings and ensure proper coding style provided that they have a reliable CDC and lint engine.”

Vic Kulkarni provided me with an answer based on sound an detailed technical theory that lead to the following: “There are over 20 techniques to reduce power consumption which must be employed during all the design phases from system level (Figure 1), RTL to gate level sign-off to model and analyze power consumption levels and provide methodologies to meet power budgets, at the same time do the balancing act of managing trade-offs associated with each technique that will be used throughout the design flow Unfortunately there is NO single silver bullet to reduce power!

Fig. 1. A holistic approach for low-power IP and IP-based SoC design from system to final sign-off with associated trade-offs [Source: ANSYS-Apache Design]

To successfully reduce power, increase signal bandwidth, and manage cost, it is essential to simultaneously optimize across the system, chip, package, and the board. As chips migrate to sub-20 nanometer (nm) process nodes and use stacked-die technologies, the ability to model and accurately predict the power/ground noise and its impact on ICs is critical for the success of advanced low-power designs and associated systems.

Design engineers must meet power budgets for a wide variety of operating conditions.  For example, a chip for a smart phone must be tested to ensure that it meets power budget requirements in standby, dormant, charging, and shutdown modes.  A comprehensive power budgeting solution is required to accurately analyze power values in numerous operating modes (or scenarios) while running all potential applications of the system.”

Jon McDonald described Mentor’s approach.  He highlighted the need for a feedback loop between architectural analysis and implementation. “Implementation optimizations focus on the most efficient power implementation of a specific architecture. This level of optimizations can find a localized minimum power usage, but are limited by their inability to make system-wide architectural trade-offs and run real world scenarios.

Software optimizations involve efforts by software designers to use the system hardware in the most power efficient manner. However, as the hardware is fixed there are significant limitations on the kinds of changes that can be made. Also, since the prototype is already available, completing the software becomes the limiting factor to completing the system. As well, software often has been developed before a prototype is available or is being reused from prior generations of a design. Going back and rewriting this software to optimize for power is generally not possible due to time constraints on completing the system integration.

Both of these areas of power optimization focus can be vastly improved by investing more in power analysis at the system level – before architectural decisions have been locked into an implementation. Modeling power as part of a transaction-level model provides quantitative feedback to design architects on the effect their decisions have on system power consumption. It also provides feedback to software developers regarding how efficiently they use the hardware platform. Finally, the data from the software execution on the platform can be used to refine the architectural choices made in the context of the actual software workloads.

Being able to optimize the system-level architecture with quantitative feedback tightly coupled to the workload (Figure 2) allows the impact of hardware and software decisions to be measured when those decisions are made. Thus, system-level power analysis exposes the effect of decisions on system wide power consumption, making them obvious and quantifiable to the hardware and software engineers.”

Figure 2. System Level Power Optimization (Courtesy of Mentor Graphics)

Drew Wingard of Sonics underscored the advantage of having in-depth knowledge of the dynamics of Network On Chip (NOC) use.

“Required levels of power savings, especially in battery-powered SOC devices, can be simplified by exploiting knowledge the on-chip network fabric inherently contains about the transactional state of the system and applying it to effective power management (Figure 3). Advanced on-chip networks provide the capability for hardware-controlled, safe shutdown of power domains without reliance on driver software probing the system. A hardware-controlled power management approach leveraging the on-chip network intelligence is superior to a software approach that potentially introduces race conditions and delays in power shut down.”

Figure 3.On-Chip Network Power Management (courtesy of Sonics)

“The on-chip network has the address decoders for the system, and therefore is the first component in the system to know the target when a transaction happens. The on-chip network provides early indication to the SOC Power Manager that a transaction needs to use a resource, for example, in a domain that’s currently not being clocked or completely powered off. The Power Manager reacts very quickly and recovers domains rapidly enough that designers can afford to set up components in a normally off state (Dark Silicon) where they are powered down until a transaction tries to access them.

Today’s SOC integration is already at levels where designers cannot afford to have power to all the transistors available at the same time because of leakage. SOC designers should view the concept of Dark Silicon as a practical opportunity to achieve the highest possible power savings. Employing the intelligence of on-chip networks for active power management, SOC designers can set up whole chip regions with the power normally off and then, transparently wake up these chip domains from the hardware.”


The Green movement should be proud of its success in underlying the importance of energy conservation.  Low Power designs, I am sure, was not one of its main objective, yet the vast majority of electronic circuits today are designed with the goal of minimizing power consumption.  All is possible, or nearly so, when consumers demand it and, importantly, are willing to pay for it.

On-Chip Communications Survey Results

Wednesday, December 19th, 2012

This comprehensive report takes a closer look at general technology trends and factors associated with OCCNs, such as core target speeds. It investigates the most popular OCCN topologies being considered for implementation in multi-core SoCs, including networks-on-chip (NoCs), crossbars, peripheral interconnect, and multi-layer bus matrices. It then dives deeper into NoCs, including analyzing adoption plans.

The topics covered in this report are:

  1. Survey methodology.
  2. Average time spent designing, modifying, verifying on-chip communications networks.
  3. Challenges when implementing on-chip communication networks.
  4. Target core speeds for 2013 design starts.
  5. Power domains partitioning expected for SoCs.
  6. On-chip network topologies being considered.
  7. Commercial NoCs implementation plans for 2013.
  8. Number of cores where commercial NoCs become an important consideration.
  9. T op criteria for selecting NoC.
  10. Primary reasons to utilize Virtual Channels.
  11. Summary.

To view the survey results, click here.

An Analysis Of Blocking Vs. Non-Blocking Flow Control In On-Chip Networks

Thursday, November 29th, 2012

High end System-on-Chip (SoC) architectures consist of tens of processing engines. These processing engines have varied traffic profiles consisting of priority traffic that require that the latency of the traffic is minimized, controlled bandwidth traffic that require low service jitter on the throughput, and best effort traffic that can tolerate highly variable service. In this paper, we investigate the trade-off between multi-threaded non-blocking (MTNB) flow-control and single threaded tag (STT) based flow-control in the realm of Open Core Protocol (OCP) [1] specifications. Specifically, we argue that the non-blocking multi-threaded flow-control protocol is more suitable for latency minimization of the priority traffic and jitter minimization of controlled bandwidth traffic, when compared with a single threaded tag (STT) based protocol. We present experimental results comparing MTNB against STT based protocols on representative DTV data flows. On average, in the STT based system, the latency of priority traffic is increased by 2.73 times and the latency of controlled bandwidth traffic is increased by 1.14 times when compared to the MTNB system, under identical configurations.

To read more, click here.

Proprietary On-Chip Connections Yield To NoC Designs

Thursday, September 22nd, 2011

By John Blyler
Interconnect technologies are nothing new at Intel. During the recent Intel Developers Forum (IDF) 2011, several processor-centric interconnect technologies were on display in the company’s Labs Pavilion. Most noticeable of these were Many Core Application Research Community (MARC) and its derivative called the Many Integrated Cores (MIC) projects.

In terms of interconnect fabric, the MARC platform relies on an open standard “Message Parsing Interface” (MPI) to communicate between as many as 48 Pentium cores within a single die. The goal of this research is to develop the interconnect hardware and parallel software applications that would support the “millions of processor” program. In this activity, Intel has been working with the U.S. government on a project called Ubiquitous High-Performance Computing (UHPC).

Interconnect strategies change as vendors move from processor-centric to SoC third-party IP-based designs. While Intel laid out its SoC development strategy years ago, few details concerning the interconnect fabric have been made public. Bill Leszinske, the company’s general manager of technical planning and business development at the Atom processor SoC development group, recently revealed that the Intel interconnect fabric will serve as a “chassis” within which a variety Intel and third-party IP can be swapped in and out for different applications. The company calls this proprietary chassis the Intel On-Chip System Fabric (IOSF). It is analogous to the ARM community’s Advanced Microcontroller Bus Architecture (AMBA) interconnect platform. Other proprietary on-chip bus structures include MIPS SoC-it and IBM’s CoreConnect, to mention a few. These buses have bridging capabilities to ARM’s AMBA bus or the Open Core Protocol (OCP) standard for IP cores (OCP-IP) socket technology.

Leszinske is quoted as saying that the IOSF is a scalable fabric that supports multicore operation and maintains the PCI-bus order. This last item is critical because Intel’s Atom processor uses the PCI bus to connect to the outside world, for example, to provide embedded programmability via Altera’s FPGA core (see, “Intel Teams Up with Altera.”) The popular PCI bus is also an important interface between ARM processors of Xilinx FPGA fabric (see, “FPGAs Move to IP through Processor Interface”).

NoC vs. internal buses
The growing demand for low power and high performance chips is putting new demands on the on-chip IP interconnect architecture. Perhaps that is why many chip companies have migrated from internal interconnect technology to on-chip networks. This approach allows them to protect their legacy IP cores and any proprietary communication features while providing access to third party IP vendors. But how do overall SoC networks, such as a network-on-chip, relate to proprietary buses like Intel’s IOSF or ARM’s AMBA?

Drew Wingard, Sonics’ CTO, puts it this way: “Our principal competitor is internal technology, which is typically derived from either legacy computer buses or the various flavors of ARM’s AMBA specifications. Intel’s IOSF represents such an internal technology, and their press interviews about IOSF make it clear that supporting the ordering requirements of PCI is crucial to them for supporting their large, existing software base.”

Processor-centric companies like ARM and Intel need interconnect architectures to grow an ecosystem of third party IP providers. But these providers have widely varying communication requirements that are difficult to manage.

Here is where NoCs can be of great value. As chief architect and co-founder of Arteris, Phillippe Boucard explains that before NoC technology was available IDMs would use hybrid-bus technology to connect IPs to a centralized crossbar, which would then route the traffic throughout the chip. In the past five years, NoC on-chip interconnect architectures began to replace proprietary hybrid bus technology.

“Our NoC IP uses Network Interface Units to convert the ARM protocol into a packetized protocol format. Instead of having a centralized crossbar, the NoC interconnects are distributed throughout the SoC. On top of that, the NoC provides several services, such as security, quality of service, software bring-up, power management, domain management, and so forth.”

There are multiple challenge facing today’s SoC designers. Chips must meet the often-conflicting requirements of low power, high performance, small die size, low cost, low heat generation and development in a very tight time-to-market period. The problem with traditional, proprietary hybrid-bus interconnects is that any change in the IP requires a physical change in the overall system topology, including the buses. With a NoC architecture, only the interconnect needs to be reconfigured.

Complex designs have spurred the growth of design re-use via semiconductor IP. To handle all of this IP, on-chip interconnects had to become more complex. Proprietary internal buses have been giving way to more open on-chip interconnect specifications. NoCs further reduce chip complexity by providing a easily reconfigured communication subsystem between the majority of IP cores on an SoC.

An Analysis Of Blocking Vs. Non-Blocking Flow Control In On-Chip Networks

Thursday, April 28th, 2011

High end System-on-Chip (SoC) architectures consist of tens of processing engines. These processing engines have varied traffic profiles consisting of priority traffic that require that the latency of the traffic is minimized, controlled bandwidth traffic that require low service jitter on the throughput, and best effort traffic that can tolerate highly variable service. In this paper, we investigate the trade-off between multi-threaded non-blocking (MTNB) flow-control and single threaded tag (STT) based flow-control in the realm of Open Core Protocol (OCP) [1] specifications. Specifically, we argue that the non- blocking multi-threaded flow-control protocol is more suitable for latency minimization of the priority traffic and jitter minimization of controlled bandwidth traffic, when compared with a single threaded tag (STT) based protocol. We present experimental results comparing MTNB against STT based protocols on representative DTV data flows. On average, in the STT based system, the latency of priority traffic is increased by 2.73 times and the latency of controlled bandwidth traffic is increased by 1.14 times when compared to the MTNB system, under identical configurations.

To download this white paper, click here.