17. Issues for Nuclear Power Plants Steam Generators

Open Access Boo

Issues for Nuclear Power Plants Steam Generators

Open Access Boo

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

WaveScript: A Case-Study in Applying a Distributed Stream-Processing Language

Applications that combine live data streams with embedded, parallel,and distributed processing are becoming more commonplace. WaveScriptis a domain-specific language that brings high-level, type-safe,garbage-collected programming to these domains. This is made possibleby three primary implementation techniques. First, we employ a novelevaluation strategy that uses a combination of interpretation andreification to partially evaluate programs into stream dataflowgraphs. Second, we use profile-driven compilation to enable manyoptimizations that are normally only available in the synchronous(rather than asynchronous) dataflow domain. Finally, we incorporatean extensible system for rewrite rules to capture algebraic propertiesin specific domains (such as signal processing).We have used our language to build and deploy a sensor-network for theacoustic localization of wild animals, in particular, theYellow-Bellied marmot. We evaluate WaveScript's performance on thisapplication, showing that it yields good performance on both embeddedand desktop-class machines, including distributed execution andsubstantial parallel speedups. Our language allowed us to implementthe application rapidly, while outperforming a previous Cimplementation by over 35%, using fewer than half the lines of code.We evaluate the contribution of our optimizations to this success