A Lazy Engine for High-utilization and Energy-efficient ReRAM-based Neural Network Accelerator

Resistive random-access memory (ReRAM) has been explored to be a promising solution to accelerate the inference of deep neural networks at the embedded systems by performing computations in memory. To reduce the latency of the neural network, all the pre-trained weights are pre-programmed in ReRAM cells as device resistance for the inference phase. However, the system utilization is decreased by the data dependency of the deployed neural networks and results in low energy efficiency. In this work, we propose a Lazy Engine for providing high utilization and energy-efficient ReRAM-based accelerators. Instead of avoiding idle time by applying ReRAM crossbar duplication, Lazy Engine delays the start time of the vector-matrix multiplication operations, with run-time programming overhead consideration, to reclaim idle time for energy efficiency while improving resource utilization. The experimental results show that Lazy Engine achieves up to 77% and 96% improvement in resource utilization and energy saving compared to state-of-the-art ReRAM-based accelerators.