High-Performance low-vcc in-order core

Power density grows in new technology nodes, thus requiring Vcc to scale especially in mobile platforms where energy is critical. This paper presents a novel approach to decrease Vcc while keeping operating frequency high. Our mechanism is referred to as immediate read after write (IRAW) avoidance. We propose an implementation of the mechanism for an Intel® SilverthorneTM in-order core. Furthermore, we show that our mechanism can be adapted dynamically to provide the highest performance and lowest energy-delay product (EDP) at each Vcc level. Results show that IRAW avoidance increases operating frequency by 57% at 500mV and 99% at 400mV with negligible area and power overhead (below 1%), which translates into large speedups (48% at 500mV and 90% at 400mV) and EDP reductions (0.61 EDP at 500mV and 0.33 at 400mV).Peer ReviewedPostprint (published version

Performance-effective operation below Vcc-min

Continuous circuit miniaturization and increased process variability point to a future with diminishing returns from dynamic voltage scaling. Operation below Vcc-min has been proposed recently as a mean to reverse this trend. The goal of this paper is to minimize the performance loss due to reduced cache capacity when operating below Vcc-min. A simple method is proposed: disable faulty blocks at low voltage. The method is based on observations regarding the distributions of faults in an array according to probability theory. The key lesson, from the probability analysis, is that as the number of uniformly distributed random faulty cells in an array increases the faults increasingly occur in already faulty blocks. The probability analysis is also shown to be useful for obtaining insight about the reliability implications of other cache techniques. For one configuration used in this paper, block disabling is shown to have on the average 6.6% and up to 29% better performance than a previously proposed scheme for low voltage cache operation. Furthermore, block-disabling is simple and less costly to implement and does not degrade performance at or above Vcc-min operation. Finally, it is shown that a victim-cache enables higher and more deterministic performance for a block-disabled cache

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc

Power Management Techniques for Data Centers: A Survey

With growing use of internet and exponential growth in amount of data to be stored and processed (known as 'big data'), the size of data centers has greatly increased. This, however, has resulted in significant increase in the power consumption of the data centers. For this reason, managing power consumption of data centers has become essential. In this paper, we highlight the need of achieving energy efficiency in data centers and survey several recent architectural techniques designed for power management of data centers. We also present a classification of these techniques based on their characteristics. This paper aims to provide insights into the techniques for improving energy efficiency of data centers and encourage the designers to invent novel solutions for managing the large power dissipation of data centers.Comment: Keywords: Data Centers, Power Management, Low-power Design, Energy Efficiency, Green Computing, DVFS, Server Consolidatio

Concertina: Squeezing in cache content to operate at near-threshold voltage

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Scaling supply voltage to values near the threshold voltage allows a dramatic decrease in the power consumption of processors; however, the lower the voltage, the higher the sensitivity to process variation, and, hence, the lower the reliability. Large SRAM structures, like the last-level cache (LLC), are extremely vulnerable to process variation because they are aggressively sized to satisfy high density requirements. In this paper, we propose Concertina, an LLC designed to enable reliable operation at low voltages with conventional SRAM cells. Based on the observation that for many applications the LLC contains large amounts of null data, Concertina compresses cache blocks in order that they can be allocated to cache entries with faulty cells, enabling use of 100 percent of the LLC capacity. To distribute blocks among cache entries, Concertina implements a compression- and fault-aware insertion/replacement policy that reduces the LLC miss rate. Concertina reaches the performance of an ideal system implementing an LLC that does not suffer from parameter variation with a modest storage overhead. Specifically, performance degrades by less than 2 percent, even when using small SRAM cells, which implies over 90 percent of cache entries having defective cells, and this represents a notable improvement on previously proposed techniques.Peer ReviewedPostprint (author's final draft

Exceeding Conservative Limits: A Consolidated Analysis on Modern Hardware Margins

Modern large-scale computing systems (data centers, supercomputers, cloud and edge setups and high-end cyber-physical systems) employ heterogeneous architectures that consist of multicore CPUs, general-purpose many-core GPUs, and programmable FPGAs. The effective utilization of these architectures poses several challenges, among which a primary one is power consumption. Voltage reduction is one of the most efficient methods to reduce power consumption of a chip. With the galloping adoption of hardware accelerators (i.e., GPUs and FPGAs) in large datacenters and other large-scale computing infrastructures, a comprehensive evaluation of the safe voltage reduction levels for each different chip can be employed for efficient reduction of the total power. We present a survey of recent studies in voltage margins reduction at the system level for modern CPUs, GPUs and FPGAs. The pessimistic voltage guardbands inserted by the silicon vendors can be exploited in all devices for significant power savings. On average, voltage reduction can reach 12% in multicore CPUs, 20% in manycore GPUs and 39% in FPGAs.Comment: Accepted for publication in IEEE Transactions on Device and Materials Reliabilit

Combining RAM technologies for hard-error recovery in L1 data caches working at very-low power modes

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Low-power modes in modern microprocessors rely on low frequencies and low voltages to reduce the energy budget. Nevertheless, manufacturing induced parameter variations can make SRAM cells unreliable producing hard errors at supply voltages below Vccmin. Recent proposals provide a rather low fault-coverage due to the fault coverage/overhead trade-off. We propose a new faulttolerant L1 cache, which combines SRAM and eDRAM cells in L1 data caches to provide 100% SRAM hard-error fault coverage. Results show that, compared to a conventional cache and assuming 50% failure probability at low-power mode, leakage and dynamic energy savings are by 85% and 62%, respectively, with a minimal impact on performance.This work was supported by the Spanish MICINN (TIN2010-18368) with the Consolider-Ingenio 2010 Programme co-funded by the European Commission FEDER funds (CSD2006-00046) and co-funded with the Plan E funds (TIN2009-14475-C04-01). Additionaly, it was supported by Generalitat de Catalunya (2009SGR1250), by FP7 program of the European Commission (TRAMS-248789), and by Spanish MINECO (TIN2012-38341-C04-01).Lorente Garcés, VJ.; Valero Bresó, A.; Sahuquillo Borrás, J.; Petit Martí, SV.; Canal, R.; López Rodríguez, PJ.; Duato Marín, JF. (2013). Combining RAM technologies for hard-error recovery in L1 data caches working at very-low power modes. IEEE, ACM. https://doi.org/10.7873/DATE.2013.031

Enhancing Performance and Energy Consumption of HER Caches by Adding Associativity

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-54420-0_45Unlike other previous techniques, the recently proposed Hard Error Recovery (HER) fault-tolerant cache provides 100% fault-coverage in L1 data caches. This full coverage makes the HER cache appropiate for fault-dominated future technology nodes. An n-way set-associative HER cache implements one cache way with fast SRAM banks and the remaining ways with eDRAM banks to address power and area. Since the number of eDRAM cache blocks used in a specific HER cache organization depends on the cache associativity (i.e., the implemented number of ways), we expect that the performance and energy consumption provided by a given HER cache design strongly depends on the cache geometry. In this work we study the behavior of the HER cache design when applied to a highly associative L1 cache like those found in some modern microprocessors. In particular this work explores a 32KB 8-way associative L1 data cache such as the one used in Intel Haswell microarchitecture. Experimental results show that, at low-power modes compared to a conventional cache with the same storage capacity and number of ways, area, leakage power, and dynamic energy savings of a 4-way HER cache are by 25%, 85%, and 62%, respectively. These percentages are further improved (by 40%, 89%, and 68%, respectively) when the cache associativity is increased to 8 ways, while the performance loss with respect to both an 8-way conventional cache and the 4-way HER cache is minimal.This work was supponed by Generalitat de Catalunya (200950R1250), by FP7 program of the European Commission (TRAMS-248789), by Spanish Ministerio de Economía y Competitividad (MINECO) and by FEDER funds under Grant TlN2012-38341-C04-01 and TIN2010-18368.Lorente Garcés, VJ.; Valero Bresó, A.; Canal, R. (2014). 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