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

HW-SW Emulation Framework for Temperature-Aware Design in MPSoCs

New tendencies envisage Multi-Processor Systems-On-Chip (MPSoCs) as a promising solution for the consumer electronics market. MPSoCs are complex to design, as they must execute multiple applications (games, video), while meeting additional design constraints (energy consumption, time-to-market). Moreover, the rise of temperature in the die for MPSoCs can seriously affect their final performance and reliability. In this paper, we present a new hardware-software emulation framework that allows designers a complete exploration of the thermal behavior of final MPSoC designs early in the design flow. The proposed framework uses FPGA emulation as the key element to model the hardware components of the considered MPSoC platform at multi-megahertz speeds. It automatically extracts detailed system statistics that are used as input to our software thermal library running in a host computer. This library calculates at run-time the temperature of on-chip components, based on the collected statistics from the emulated system and the final floorplan of the MPSoC. This enables fast testing of various thermal management techniques. Our results show speed-ups of three orders of magnitude compared to cycle-accurate MPSoC simulator

CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

Caracterización y optimización térmica de sistemas en chip mediante emulación con FPGAs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 15/06/2012Tablets and smartphones are some of the many intelligent devices that dominate the consumer electronics market. These systems are complex to design as they must execute multiple applications (e.g.: real-time video processing, 3D games, or wireless communications), while meeting additional design constraints, such as low energy consumption, reduced implementation size and, of course, a short time-to-market. Internally, they rely on Multi-processor Systems on Chip (MPSoCs) as their main processing cores, to meet the tight design constraints: performance, size, power consumption, etc. In a bad design, the high logic density may generate hotspots that compromise the chip reliability. This thesis introduces a FPGA-based emulation framework for easy exploration of SoC design alternatives. It provides fast and accurate estimations of performance, power, temperature, and reliability in one unified flow, to help designers tune their system architecture before going to silicon.El estado del arte, en lo que a diseño de chips para empotrados se refiere, se encuentra dominado por los multi-procesadores en chip, o MPSoCs. Son complejos de diseñar y presentan problemas de disipación de potencia, de temperatura, y de fiabilidad. En este contexto, esta tesis propone una nueva plataforma de emulación para facilitar la exploración del enorme espacio de diseño. La plataforma utiliza una FPGA de propósito general para acelerar la emulación, lo cual le da una ventaja competitiva frente a los simuladores arquitectónicos software, que son mucho más lentos. Los datos obtenidos de la ejecución en la FPGA son enviados a un PC que contiene bibliotecas (modelos) SW para calcular el comportamiento (e.g.: la temperatura, el rendimiento, etc...) que tendría el chip final. La parte experimental está enfocada a dos puntos: por un lado, a verificar que el sistema funciona correctamente y, por otro, a demostrar la utilidad del entorno para realizar exploraciones que muestren los efectos a largo plazo que suceden dentro del chip, como puede ser la evolución de la temperatura, que es un fenómeno lento que normalmente requiere de costosas simulaciones software.Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Emulation-based transient thermal modeling of 2D/3D systems-on-chip with active cooling

State-of-the-art devices in the consumer electronics market are relying more and more on Multi-Processor Systems-On-Chip (MPSoCs) as an efficient solution to meet their multiple design constrains, such as low cost, low power consumption, high performance and short time-to-market. In fact, as technology scales down, logic density and power density increase, generating hot spots that seriously affect the MPSoC performance and can physically damage the final system behavior. Moreover, forthcoming three-dimensional (3D) MPSoCs can achieve higher system integration density, but the aforementioned thermal problems are seriously aggravated. Thus, new thermal exploration tools are needed to study the temperature variation effects inside 3D MPSoCs. In this paper, we present a novel approach for fast transient thermal modeling and analysis of 3D MPSoCs with active (liquid) cooling solutions, while capturing the hardware-software interaction. In order to preserve both accuracy and speed, we propose a close-loop framework that combines the use of Field Programmable Gate Arrays (FPGAs) to emulate the hardware components of 2D/3D MPSoC platforms with a highly optimized thermal simulator, which uses an RC-based linear thermal model to analyze the liquid flow. The proposed framework offers speed-ups of more than three orders of magnitude when compared to cycle-accurate 3D MPSoC thermal simulators. Thus, this approach enables MPSoC designers to validate different hardware- and software-based 3D thermal management policies in real-time, and while running real-life applications, including liquid cooling injection contro

ControlPULP: A RISC-V On-Chip Parallel Power Controller for Many-Core HPC Processors with FPGA-Based Hardware-In-The-Loop Power and Thermal Emulation

High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output (MIMO) optimal power requirements, high-end processors integrate an on-die power controller system (PCS). While traditional PCSs are based on a simple microcontroller (MCU)-class core, more scalable and flexible PCS architectures are required to support advanced MIMO control algorithms for managing the ever-increasing number of cores, power states, and process, voltage, and temperature variability. This paper presents ControlPULP, an open-source, HW/SW RISC-V parallel PCS platform consisting of a single-core MCU with fast interrupt handling coupled with a scalable multi-core programmable cluster accelerator and a specialized DMA engine for the parallel acceleration of real-time power management policies. ControlPULP relies on FreeRTOS to schedule a reactive power control firmware (PCF) application layer. We demonstrate ControlPULP in a power management use-case targeting a next-generation 72-core HPC processor. We first show that the multi-core cluster accelerates the PCF, achieving 4.9x speedup compared to single-core execution, enabling more advanced power management algorithms within the control hyper-period at a shallow area overhead, about 0.1% the area of a modern HPC CPU die. We then assess the PCS and PCF by designing an FPGA-based, closed-loop emulation framework that leverages the heterogeneous SoCs paradigm, achieving DVFS tracking with a mean deviation within 3% the plant's thermal design power (TDP) against a software-equivalent model-in-the-loop approach. Finally, we show that the proposed PCF compares favorably with an industry-grade control algorithm under computational-intensive workloads.Comment: 33 pages, 11 figure

Exploring manycore architectures for next-generation HPC systems through the MANGO approach

[EN] The Horizon 2020 MANGO project aims at exploring deeply heterogeneous accelerators for use in High-Performance Computing systems running multiple applications with different Quality of Service (QoS) levels. The main goal of the project is to exploit customization to adapt computing resources to reach the desired QoS. For this purpose, it explores different but interrelated mechanisms across the architecture and system software. In particular, in this paper we focus on the runtime resource management, the thermal management, and support provided for parallel programming, as well as introducing three applications on which the project foreground will be validated.This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 671668.Flich Cardo, J.; Agosta, G.; Ampletzer, P.; Atienza-Alonso, D.; Brandolese, C.; Cappe, E.; Cilardo, A.... (2018). Exploring manycore architectures for next-generation HPC systems through the MANGO approach. Microprocessors and Microsystems. 61:154-170. https://doi.org/10.1016/j.micpro.2018.05.011S1541706