Stochastic-based Neural Network hardware acceleration for an efficient ligand-based virtual screening

by   Christian F. Frasser, et al.

Artificial Neural Networks (ANN) have been popularized in many science and technological areas due to their capacity to solve many complex pattern matching problems. That is the case of Virtual Screening, a research area that studies how to identify those molecular compounds with the highest probability to present biological activity for a therapeutic target. Due to the vast number of small organic compounds and the thousands of targets for which such large-scale screening can potentially be carried out, there has been an increasing interest in the research community to increase both, processing speed and energy efficiency in the screening of molecular databases. In this work, we present a classification model describing each molecule with a single energy-based vector and propose a machine-learning system based on the use of ANNs. Different ANNs are studied with respect to their suitability to identify biochemical similarities. Also, a high-performance and energy-efficient hardware acceleration platform based on the use of stochastic computing is proposed for the ANN implementation. This platform is of utility when screening vast libraries of compounds. As a result, the proposed model showed appreciable improvements with respect previously published works in terms of the main relevant characteristics (accuracy, speed and energy-efficiency).


page 1

page 9


QubitHD: A Stochastic Acceleration Method for HD Computing-Based Machine Learning

Machine Learning algorithms based on Brain-inspired Hyperdimensional (HD...

ODIN: A Bit-Parallel Stochastic Arithmetic Based Accelerator for In-Situ Neural Network Processing in Phase Change RAM

Due to the very rapidly growing use of Artificial Neural Networks (ANNs)...

Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Accelerator

Machine learning, particularly deep neural network inference, has become...

Are SNNs Truly Energy-efficient? - A Hardware Perspective

Spiking Neural Networks (SNNs) have gained attention for their energy-ef...

To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration

As deep learning models scale, they become increasingly competitive from...

Machine learning using magnetic stochastic synapses

The impressive performance of artificial neural networks has come at the...

EXSCALATE: An extreme-scale in-silico virtual screening platform to evaluate 1 trillion compounds in 60 hours on 81 PFLOPS supercomputers

The social and economic impact of the COVID-19 pandemic demands the redu...

Please sign up or login with your details

Forgot password? Click here to reset