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

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 …

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 …

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 …

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

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 …

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

(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.

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

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 …