Unary Computing

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 …

Unary Computing

(UW-Madison ECE757 Guest Lecture) Present the current unary computing stack.

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 …

In-Stream Correlation-Based Division and Bit-Inserting Square Root in Stochastic Computing

Stochastic Computing (SC) has shown great promise in achieving low hardware area and power consumption for neuromorphic architectures compared to traditional binary-encoded computation, due to its bit-serial data representation and extremely …

uGEMM: Unary Computing Architecture for GEMM Applications

(ISCA'20) Present an ultra-low power unary computing architecture for GEMM, which is compatible for both temporal and rate coding.

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 …

Introduction to Unary Computing

(UW-Madison ECE757 Guest Lecture) Present the basic concept of unary computing, as well as its mechanism and application.

Unary Computing for General Matrix Multiplication

(UW-Madison Computer Architecture Affiliate Meeting) Present uGEMM architecture, a high performance uanry computing architecture to unify the computation for both rate- and temporal-coded bit streams.

In-Stream Stochastic Division and Square Root via Correlation

(DAC'19) Present division and square root design in stochastic computing, which achieve high accuracy and efficiency by leveraging stochastic correlation among bit streams.

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 …