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Saving Energy of RRAM-Based Neural Accelerator Through State-Aware Computing

In-memory computing (IMC) is recognized as one of the most promising architecture solution to realize energy-efficient neural network inference. Amongst many memory technology, resistive RAM (RRAM) is a very attractive device to implement the IMC-based neural network accelerator architecture, which is particularly suitable for power-constrained IoT systems. Due to the nature of low leakage and in-situ computing, the dynamic power consumption of dot-production operations in RRAM crossbars dominates the chip power, especially when applied to low-precision neural networks. This work investigates the correlation between the cell resistance state and the crossbar operation power, and proposes a state-aware RRAM accelerator (SARA) architecture for energy-efficient low-precision neural networks. With the proposed state-aware network training and mapping strategy, crossbars in the RRAM accelerator can perform in a lower power state. Furthermore, we also leverage the proposed RRAM accelerator architecture to reduce the power consumption of high-precision network inference with both single-level or multilevel RRAM. The evaluation results show that for binary neural networks, our design saves 40.53% RRAM computing energy on average over the baseline. For high precision neural networks, the proposed method reduces 11.67% computing energy on average without any accuracy loss.