Taylor Approximation

When Dataflows Converge: Reconfigurable and Approximate Computing for Emerging Neural Networks

Deep Neural Networks (DNNs) have gained significant attention in both academia and industry due to the superior application-level accuracy. As DNNs rely on compute- or memory-intensive general matrix multiply (GEMM) operations, approximate computing …

UNO: Virtualizing and Unifying Nonlinear Operations for Emerging Neural Networks

Linear multiply-accumulate (MAC) operations have been the main focus of prior efforts in improving the energy efficiency of neural network inference due to their dominant contribution to energy consumption in traditional models. On the other hand, …