0.3-2.6 TOPS/W实时的大规模ConvNets的可精度扩展处理器
A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets
摘要：
用于ConvNets或卷积神经网络（CNN）的低功耗精度可扩展处理器采用40 nm技术实现。它的256个并行处理单元在204 MHz下运行时达到峰值102 GOPS。为了在保持吞吐量的同时最大限度地减少能耗，这是第一个利用卷积的稀疏性并实现动态精度可扩展性，从而实现电源和能量缩放的作品。该处理器是完全C可编程的，在204 MHz时功耗为25- 288 mW，效率范围为0.3-2.6 real TOPS/W。因此，该系统的能效比最先进的系统高出3.9倍。
英文摘要：
A low-power precision-scalable processor for ConvNets or convolutional neural networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing units achieve a peak 102GOPS running at 204MHz. To minimize energy consumption while maintaining throughput, this works is the first to both exploit the sparsity of convolutions and to implement dynamic precision-scalability enabling supply- and energy scaling. The processor is fully C-programmable, consumes 25-288mW at 204 MHz and scales efficiency from 0.3-2.6 real TOPS/W. This system hereby outperforms the state-of-the-art up to 3.9x in energy efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
