X-NVDLA: Runtime Accuracy Configurable NVDLA Based on Applying Voltage Overscaling to Computing and Memory Units

Abstract

This paper investigates a runtime accuracy reconfigurable implementation of an energy efficient deep learning accelerator. It is based on voltage overscaling (VOS) technique which provides dynamic adjustment of approximation level as well as improving lifetime/reliability of the accelerator. The technique is applied to both computing and memory units where based on the minimum required accuracy, the applied voltage is adjusted during the runtime. The implementation of the network is performed using NVDLA which is an open-source CNN (Convolutional Neural Network) accelerator. The approximation is applied to both the accelerator MAC array employed for the required network computations and to the accelerator SRAM memory utilized for storing the inputs (images), weights, and activation data. To control the accuracy degradation of the approximate accelerator (called X-NVDLA), the reduced voltage is applied only to LSB bits of the MAC array and SRAM unit. To assess the efficacy of the proposed energy efficient accelerator, the energy-accuracy characteristics of X-NVDLA when running LeNet-5 and ResNet-50 networks with 8-bit (integer) precision are investigated. In addition, the characteristic of bias temperature instability (BTI), as one of the lifetime deteriorating phenomena is determined. The study includes energy improvement versus accuracy degradation as a function of overscaled voltages and number of approximate the least significant bits using a 15nm FinFET technology.