A 12.08-TOPS/W All-Digital Time-Domain CNN Engine Using Bi-Directional Memory Delay Lines for Energy Efficient Edge Computing

In this article, we demonstrate an energy efficient convolutional neural network (CNN) engine by performing multiply-and-accumulate (MAC) operations in the time domain. The multi-bit inputs are compactly represented as a single pulse width encoded input. This translates into reduced switching capacitance ( $C_{\mathrm{ DYN}}$ ), compared to baseline digital implementation, and can enable low power neural network computing in an edge device. The time-domain CNN engine employs a novel bi-directional memory delay line (MDL) unit to perform signed accumulation of input and weight products. The proposed MDL design leverages standard digital circuits and does not require any capacitors and complex analog-to-digital converters (ADCs) to realize the convolution operation, thereby enabling easy scaling across the process technology nodes. Four speed-up modes and a configurable MDL length are supported to address throughput versus accuracy trade-off of the time-domain computing approach. Delay calibration units have been accommodated to mitigate the process variation induced delay mismatch among concurrently operating MDL units. The proposed time-domain MDL design implements a LeNet-5 CNN engine in a commercial 40-nm CMOS process achieving an energy efficiency of 12.08 TOPS/W, a throughput of 0.365 GOPS at 537 mV in the 16 $\times $ speed-up mode. 40-nm CMOS test-chip measurements over 100 MNIST images show 97% classification accuracy. Simulation results over the entire 10 000 MNIST validation dataset images taking into account the circuit non-ideal effects of the MDL-based time-domain approach show a classification accuracy of 98.42%. The test-chip is operational down to the near-threshold voltage (up to 375 mV) while maintaining the classification accuracy over 90% in the 1 $\times $ speed-up mode. Furthermore, two methods of scaling MDLs to multi-bit weights are proposed. Simulation results for 1000-class AlexNet over 50 000 ImageNet validation dataset images show classification accuracy loss within 1% when compared with software implementation. The proposed MDL based time-domain approach performing 1-bit/8-bit weight and 8-bit input MAC operations when compared with the corresponding baseline digital implementations shows 2.09 $\times $ −2.32 $\times $ higher energy efficiency and 2.22 $\times $ −3.45 $\times $ smaller area.