Spiking Neural Networks (SNNs) have shown capabilities of achieving high
accuracy under unsupervised settings and low operational power/energy due to
their bio-plausible computations. Previous studies identified that DRAM-based
off-chip memory accesses dominate the energy consumption of SNN processing.
However, state-of-the-art works do not optimize the DRAM energy-per-access,
thereby hindering the SNN-based systems from achieving further energy
efficiency gains. To substantially reduce the DRAM energy-per-access, an
effective solution is to decrease the DRAM supply voltage, but it may lead to
errors in DRAM cells (i.e., so-called approximate DRAM). Towards this, we
propose \textit{EnforceSNN}, a novel design framework that provides a solution
for resilient and energy-efficient SNN inference using reduced-voltage DRAM for
embedded systems. The key mechanisms of our EnforceSNN are: (1) employing
quantized weights to reduce the DRAM access energy; (2) devising an efficient
DRAM mapping policy to minimize the DRAM energy-per-access; (3) analyzing the
SNN error tolerance to understand its accuracy profile considering different
bit error rate (BER) values; (4) leveraging the information for developing an
efficient fault-aware training (FAT) that considers different BER values and
bit error locations in DRAM to improve the SNN error tolerance; and (5)
developing an algorithm to select the SNN model that offers good trade-offs
among accuracy, memory, and energy consumption. The experimental results show
that our EnforceSNN maintains the accuracy (i.e., no accuracy loss for BER
less-or-equal 10^-3) as compared to the baseline SNN with accurate DRAM, while
achieving up to 84.9\% of DRAM energy saving and up to 4.1x speed-up of DRAM
data throughput across different network sizes.

本文提出了一种名为 EnforceSNN 的新型设计框架，通过降低 DRAM 供电电压，通过采用量化权重、高效的 DRAM 映射策略、分析神经网络容错性、开发高效的容错训练机制以及选择网络模型优化网络精度、内存和能量消耗等机制，实现在嵌入式系统中 SNN 推断的鲁棒能量效率，同时进行了实验验证。