571 research outputs found
An Automated Design-flow for FPGA-based Sequential Simulation
In this paper we describe the automated design flow that will transform and map a given homogeneous or heterogeneous hardware design into an FPGA that performs a cycle accurate simulation. The flow replaces the required manually performed transformation and can be embedded in existing standard synthesis flows. Compared to the earlier manually translated designs, this automated flow resulted in a reduced number of FPGA hardware resources and higher simulation frequencies. The implementation of the complete design flow is work in progress.\u
The MANGO FET-HPC Project: an overview
© 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.In this paper, we provide an overview of the MANGO project
and its goal. The MANGO project aims at addressing power, performance
and predictability (the PPP space) in future High-Performance Computing
systems. It starts from the fundamental intuition that effective
techniques for all three goals ultimately rely on customization to adapt
the computing resources to reach the desired Quality of Service (QoS).
From this starting point, MANGO will explore different but interrelated
mechanisms at various architectural levels, as well as at the level of
the system software. In particular, to explore a new positioning across
the PPP space, MANGO will investigate system-wide, holistic, proactive
thermal and power management aimed at extreme-scale energy efficiency.The MANGO project starts in October 2015 and is funded by the European Commission under the Horizon 2020 FET-HPC program. 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.; Cilardo, A.; Fornaciari, W.; Kovac, M.... (2015). The MANGO FET-HPC Project: an overview. IEEE Computer Society. https://doi.org/10.1109/CSE.2015.57
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
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
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