Energy Efficiency

uGEMM: Unary Computing for GEMM Applications

General matrix multiplication (GEMM) is pervasive in various domains, such as signal processing, computer vision, and machine learning. Conventional binary architectures for GEMM exhibit poor scalability in area and energy efficiency, due to the …

Normalized Stability: A Cross-Level Design Metric for Early Termination in Stochastic Computing

Stochastic computing is a statistical computing scheme that represents data as serial bit streams to greatly reduce hardware complexity. The key trade-off is that processing more bits in the streams yields higher computation accuracy at the cost of …

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 …

Unary Computing

An emerging computing scheme based on unary bitstreams.