Nonlinearity

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 …

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

(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 …

RAVEN

A Reconfigurable Architecture for Varying Emerging Neural Networks.

Nonlinear Stochastic Functions for Capsule Neural Network

(UW-Madison Computer Architecture Affiliate Meeting) Present design of nonlinear functions for stochastic computing, which can be used in Capsule Neural Network.