Reprogrammable Non-Linear Circuits Using ReRAM for NN Accelerators

Abstract

As the massive usage of artificial intelligence techniques spreads in the economy, researchers are exploring new techniques to reduce the energy consumption of Neural Network (NN) applications, especially as the complexity of NNs continues to increase. Using analog Resistive RAM devices to compute matrix-vector multiplication in O (1) time complexity is a promising approach, but it is true that these implementations often fail to cover the diversity of non-linearities required for modern NN applications. In this work, we propose a novel approach where Resistive RAMs themselves can be reprogrammed to compute not only the required matrix multiplications but also the activation functions, Softmax, and pooling layers, reducing energy in complex NNs. This approach offers more versatility for researching novel NN layouts compared to custom logic. Results show that our device outperforms analog and digital field-programmable approaches by up to 8.5× in experiments on real-world human activity recognition and language modeling datasets with convolutional neural network, generative pre-trained Transformer, and long short-term memory models.