Quarry: Quantization-based ADC Reduction for ReRAM-based Deep Neural Network Accelerators
Azat Azamat, Faaiz Asim, Jongeun Lee
Abstract
ReRAM (Resistive Random-Access Memory) crossbar arrays have the potential to provide extremely fast and low-cost DNN (Deep Neural Network) acceleration. However, peripheral circuits, in particular ADCs (Analog-Digital Converters), can be a large overhead and/or slow down the operation considerably. In this paper we propose to use advanced quantization techniques to reduce the ADC overhead of ReRAM crossbar arrays. Our method does not require any hardware change but can reduce the overhead of ADC greatly. Our methodology is also general, having no restriction in terms of DNN type (binarized or multi-bit) or ReRAM crossbar array size. Our experimental results using ResNet on ImageNet dataset demonstrate that our method can reduce the size of ADC by 32× compared with ISAAC at very little accuracy loss of 0.24%p.