As soft processors are increasingly used in diverse applications, there is a need to evolve their microarchitectures in a way that suits the FPGA implementation substrate. This paper compares the delay and area of a comprehensive set of processor building block circuits when implemented on custom CMOS and FPGA substrates. We then use the results of these comparisons to infer how the microarchitecture of soft processors on FPGAs should be different from hard processors on custom CMOS.We find that the ratios of the area required by an FPGA to that of custom CMOS for different building blocks varies significantly more than the speed ratios. As area is often a key design constraint in FPGA circuits, area ratios have the most impact on microarchitecture choices. Complete processor cores have area ratios of 17-27x and delay ratios of 18-26x. Building blocks that have dedicated hardware support on FPGAs such as SRAMs, adders, and multipliers are particularly area-efficient (2-7x area ratio), while multiplexers and CAMs are particularly area-inefficient (>100x area ratio), leading to cheaper ALUs, larger caches of low associativity, and more expensive bypass networks than on similar hard processors. We also find that a low delay ratio for pipeline latches (12-19x) suggests soft processors should have pipeline depths 20% greater than hard processors of similar complexity.

/pdf/comparing-fpga-vs-custom-cmos-and-the-impact-on-processor-2tkw7qirts.pdf

Comparing FPGA vs. custom cmos and the impact on processor microarchitecture

Specialization of datacenter resources brings performance and energy improvements in response to the growing scale and diversity of cloud applications. Yet heterogeneous hardware adds complexity and volatility to latency-sensitive applications. A resource allocation mechanism that leverages architectural principles can overcome both of these obstacles. We integrate research in heterogeneous architectures with recent advances in multi-agent systems. Embedding architectural insight into proxies that bid on behalf of applications, a market effectively allocates hardware to applications with diverse preferences and valuations. Exploring a space of heterogeneous datacenter configurations, which mix server-class Xeon and mobile-class Atom processors, we find an optimal heterogeneous balance that improves both welfare and energy-efficiency. We further design and evaluate twelve design points along the Xeon-to-Atom spectrum, and find that a mix of three processor architectures achieves a 12× reduction in response time violations relative to equal-power homogeneous systems.

/pdf/navigating-heterogeneous-processors-with-market-mechanisms-2ymnefv4a7.pdf

Navigating heterogeneous processors with market mechanisms

Microprocessor-based systems today are composed of multi-core, multi-threaded processors with complex cache hierarchies and gigabytes of main memory. Accurate characterization of such a system, through predictive pre-silicon modeling and/or diagnostic post silicon measurement based analysis are increasingly cumbersome and error prone. This is especially true of energy-related characterization studies. In this paper, we take the position that automated micro-benchmarks generated with particular objectives in mind hold the key to obtaining accurate energy-related characterization. As such, we first present a flexible micro-benchmark generation framework (MicroProbe) that is used to probe complex multi-core/multithreaded systems with a variety and range of energy-related queries in mind. We then present experimental results centered around an IBM POWER7 CMP/SMT system to demonstrate how the systematically generated micro-benchmarks can be used to answer three specific queries: (a) How to project application-specific (and if needed, phase-specific) power consumption with component-wise breakdowns? (b) How to measure energy-per-instruction (EPI) values for the target machine? (c) How to bound the worst-case (maximum) power consumption in order to determine safe, but practical (i.e. affordable) packaging or cooling solutions? The solution approaches to the above problems are all new. Hardware measurement based analysis shows superior power projection accuracy (with error margins of less than 2.3% across SPEC CPU2006) as well as maxpower stressing capability (with 10.7% increase in processor power over the very worst-case power seen during the execution of SPEC CPU2006 applications).

/pdf/systematic-energy-characterization-of-cmp-smt-processor-4y5dq8pw8g.pdf

Systematic Energy Characterization of CMP/SMT Processor Systems via Automated Micro-Benchmarks

In the last couple of decades, handheld wireless devices such as cell phones have become one of the most prolific electronic devices in history. With this has come an exploding demand for performance and features that cover almost every aspect of our digital multimedia interconnected lives including 3-D gaming, still and video cameras, WAN, Bluetooth, high-speed data connections, and so on. As ever increasing features continue to be integrated into these products, there is an ongoing need to develop innovative ways to reduce power consumption and extend battery life. Only through continual process and circuit cooptimization are we able to reap the benefits of technology scaling required to meet the feature and performance demands in the face of increasing process variations and exponentially increasing leakage currents. As a result, SmartReflex power and performance technologies have been developed and applied to 90 nm, 65 nm, and 45 nm system-on-chip (SoC), to achieve optimal power and performance. SmartReflex technologies consist of two major components to optimize SoC power and performance: static and dynamic techniques. Static techniques like power-gating, retention and off-mode are used to lower leakage and allow for extended battery lifetimes for standby times. Dynamic techniques such as dynamic power switching, adaptive voltage scaling, dynamic voltage/frequency scaling with split-rail memories, and adaptive body-biasing address active power and performance challenges. These techniques enable SoC solutions with the performance of the latest process technology and provide the user with advanced multimedia features with orders of magnitude of power reduction.

SmartReflex Power and Performance Management Technologies for 90 nm, 65 nm, and 45 nm Mobile Application Processors

Continued scaling of process technologies is critical to sustaining improvements in processor frequencies and performance. However, shrinking process technologies exacerbates process variations — the deviation of process parameters from their target specifications. In the context of multi-core CMPs, which are implemented to feature homogeneous cores, within-die process variations result in substantially different core frequencies. Exposing such process-variation induced heterogeneity interferes with the norm of marketing chips at a single frequency. Further, application performance is undesirably dictated by the frequency of the core it is running on. To work around these challenges, a single uniform frequency, dictated by the slowest core, is currently chosen as the chip frequency sacrificing the increased performance capabilities of cores that could operate at higher frequencies. In this paper, we propose choosing the mean frequency across all cores, in lieu of the minimum frequency, as the single-frequency to use as the chip sales frequency. We examine several scheduling algorithms implemented below the O/S in hardware/firmware that guarantee minimum application performance near that of the average frequency, by masking process-variation induced heterogeneity from the end-user. We show that our Throughput-Driven Fairness (TDF) scheduling policy improves throughput by an average of 12% compared to a naive fairness scheme (round-robin) for frequency-sensitive applications. At the same time, TDF allows 98% of chips to maintain minimum performance at or above 90% of that expected at the mean frequency, providing a single uniform performance level to present for the chip.

Achieving uniform performance and maximizing throughput in the presence of heterogeneity

This paper describes a low power Intel Architecture (IA) processor specifically designed for Mobile Internet Devices (MID) with performance similar to mainstream Ultra-Mobile PCs. The design relies on high residency in a new low-power state in order to keep average power and idle power below 220 and 80 mW, respectively. The design consists of an in-order pipeline capable of issuing 2 instructions per cycle supporting 2 threads, 32 KB instruction and 24 KB data L1 caches, independent integer and floating point execution units, x86 front end execution unit, a 512 KB L2 cache and a 533 MT/s dual-mode (GTL and CMOS) front-side-bus (FSB). The design contains 47 million transistors in a die size under 25 mm 2 manufactured in a 9-metal 45 nm CMOS process with optimized transistors for low leakage. Maximum thermal design power (TDP) consumption is measured at 2 W at 1.0 V, 90 °C using a synthetic power-virus test at a frequency of 1.86 GHz.

A Sub-2 W Low Power IA Processor for Mobile Internet Devices in 45 nm High-k Metal Gate CMOS

This paper describes a low power Intel Architecture (IA) processor specifically designed for Mobile Internet Devices (MID) with performance similar to mainstream Ultra-Mobile PCs. The design relies on high residency in a new low-power state in order to keep average power and idle power below 220 and 80 mW, respectively. The design consists of an in-order pipeline capable of issuing 2 instructions per cycle supporting 2 threads, 32 KB instruction and 24 KB data L1 caches, independent integer and floating point execution units, times86 front end execution unit, a 512 KB L2 cache and a 533 MT/s dual-mode (GTL and CMOS) front-side-bus (FSB). The design contains 47 million transistors in a die size under 25 mm2 manufactured in a 9-metal 45 nm CMOS process with optimized transistors for low leakage. Maximum thermal design power (TDP) consumption is measured at 2 W at 1.0 V, 90degC using a synthetic power-virus test at a frequency of 1.86 GHz.

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A Sub-2 W Low Power IA Processor for Mobile Internet Devices in 45 nm High-k Metal Gate CMOS

A Sub-2 W Low Power IA Processor for Mobile Internet Devices in 45 nm High-k Metal Gate CMOS