Efficient nonlinear function approximation in analog resistive crossbars for recurrent neural networks.

ABSTRACT: Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models. In this work, we experimentally demonstrate the implementation of a non-linear activation function integrated with a ramp analog-to-digital conversion (ADC) at the periphery of the memory to improve in-memory implementation of RNNs. Our ap

SUBMITTER: Yang J 

PROVIDER: S-EPMC11779922 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature