Energy Efficiency

Paper is accepted in ASP-DAC 2021

This paper introduces a metric to identify better SC units and predict the timing for early termination.

uGEMM: Unary Computing Architecture for GEMM Applications

General matrix multiplication (GEMM) is universal in various applications, such as signal processing, machine learning, and computer vision. Conventional GEMM hardware architectures based on binary computing exhibit low area and energy efficiency as …

Approximate Hardware Techniques for Energy-Quality Scaling Across the System

For error-resilient applications, such as machine learning and signal processing, a significant improvement in energy efficiency can be achieved by relaxing exactness constraint on output quality. This paper presents a taxonomy of hardware techniques …

SECO: A Scalable Accuracy Approximate Exponential Function Via Cross-Layer Optimization

From signal processing to emerging deep neural networks, a range of applications exhibit intrinsic error resilience. For such applications, approximate computing opens up new possibilities for energy-efficient computing by producing slightly …

In-Stream Stochastic Division and Square Root via Correlation

Stochastic Computing (SC) is designed to minimize hardware area and power consumption compared to traditional binary-encoded computation, stemming from the bit-serial data representation and extremely straightforward logic. Though existing Stochastic …

Unary Computing

An emerging computing scheme based on unary bit streams.