Convolutional Neural Networks (CNNs) demonstrate great performance in various applications but have high computational complexity. Quantization is applied to reduce the latency and storage cost of CNNs. Among the quantization methods, Binary and Ternary Weight Networks (BWNs and TWNs) have a unique advantage over 8-bit and 4-bit quantization. They replace the multiplication operations in CNNs with additions, which are favoured on In-Memory-Computing (IMC) devices. IMC acceleration for BWNs has been widely studied. However, though TWNs have higher accuracy and better sparsity, IMC acceleration for TWNs has limited research. TWNs on existing IMC devices are inefficient because the sparsity is not well utilized, and the addition operation is not efficient. In this paper, we propose FAT as a novel IMC accelerator for TWNs. First, we propose a Sparse Addition Control Unit, which utilizes the sparsity of TWNs to skip the null operations on zero weights. Second, we propose a fast addition scheme based on the memory Sense Amplifier to avoid the time overhead of both carry propagation and writing back the carry to the memory cells. Third, we further propose a Combined-Stationary data mapping to reduce the data movement of both activations and weights and increase the parallelism of memory columns. Simulation results show that for addition operations at the Sense Amplifier level, FAT achieves 2.00X speedup, 1.22X power efficiency and 1.22X area efficiency compared with State-Of-The-Art IMC accelerator ParaPIM. FAT achieves 10.02X speedup and 12.19X energy efficiency compared with ParaPIM on networks with 80% sparsity

本文提出了一种新型IMC加速器FAT，其中使用Sparse Addition Control Unit来利用TWNs的稀疏性，使用快速加法方案来避免传输和存储时间开销，并使用Combined-Stationary数据映射来提高内存列中的并行性，模拟结果表明，FAT相比于现有的IMC加速器ParaPIM可以实现2.00X的加速，1.22X的功率效率和1.22X的面积效率，对于80％平均稀疏度的网络，FAT可以实现10.02X的加速和12.19X的能量效率。

FAT: 一种用于三元权重神经网络的快速加速器