论文标题
数据驱动的神经形态DRAM基于CNN和RNN加速器
Data-Driven Neuromorphic DRAM-based CNN and RNN Accelerators
论文作者
论文摘要
在硬件加速器上运行大型深神经网络（DNN）所消耗的能量主要是需要大量快速记忆来存储状态和权重。目前，这种庞大的必需记忆仅通过DRAM在经济上可行。尽管DRAM是高通量和低成本存储器（比SRAM低20倍），但其长期的随机访问延迟对尖峰神经网络（SNNS）中不可预测的访问模式不利。此外，从DRAM访问数据的成本数量级要比使用该数据进行算术多。如果可用的本地记忆，并且生成很少的峰值，则SNN可以节能。本文报道了我们在过去5年中的发展卷积和经常性的深神经网络硬件加速器，这些加速器可利用类似于SNN的空间或时间稀疏性，但即使使用DRAM来实现SOA吞吐量，功率效率和延迟，即使使用DRAM来存储大型DNN的权重和州所需的存储。
The energy consumed by running large deep neural networks (DNNs) on hardware accelerators is dominated by the need for lots of fast memory to store both states and weights. This large required memory is currently only economically viable through DRAM. Although DRAM is high-throughput and low-cost memory (costing 20X less than SRAM), its long random access latency is bad for the unpredictable access patterns in spiking neural networks (SNNs). In addition, accessing data from DRAM costs orders of magnitude more energy than doing arithmetic with that data. SNNs are energy-efficient if local memory is available and few spikes are generated. This paper reports on our developments over the last 5 years of convolutional and recurrent deep neural network hardware accelerators that exploit either spatial or temporal sparsity similar to SNNs but achieve SOA throughput, power efficiency and latency even with the use of DRAM for the required storage of the weights and states of large DNNs.
