An Improved K-Spare Decomposing Algorithm for Mapping Neural Networks onto Crossbar-Based Neuromorphic Computing Systems

Abstract

Mapping deep neural network (DNN) models onto crossbar-based neuromorphic computing system (NCS) has recently become more popular since it allows us to realize the advantages of DNNs on small computing systems. However, due to the physical limitations of NCS, such as limited programmability, or a fixed and small number of neurons and synapses of memristor crossbars (the most important component of NCS), we have to quantize and decompose a DNN model into many partitions before the mapping. However, each weight parameter in the original network has its own scaling factor, while crossbar cell hardware has only one scaling factor. This will cause a significant error and will reduce the performance of the system. To mitigate this issue, the K-spare neuron approach has been proposed, which uses additional K spare neurons to capture more scaling factors. Unfortunately, this approach typically uses a large number of neurons overhead. To mitigate this issue, this paper proposes an improved version of the K-spare neuron method that uses a decomposition algorithm to minimize the neuron number overhead while maintaining the accuracy of the DNN model. We achieve this goal by using a mean squared quantization error (MSQE) to evaluate which crossbar units are more important and use more scaling factor than others, instead of using the same k-spare neurons for all crossbar cells as previous work does. Our experimental results are demonstrated on the ImageNet dataset (ILSVRC2012) and three typical and popular deep convolution neural networks: VGG16, Resnet152, and MobileNet v2. Our proposed method only uses 0.1%, 3.12%, and 2.4% neurons overhead for VGG16, Resnet152, and MobileNet v2 to keep their accuracy loss at 0.44%, 0.63%, and 1.24%, respectively, while other methods use about 10–20% of neurons overhead for the same accuracy loss.