Andrew Cassidy

Overview

Title

Principle Research Scientist, Brain-Inspired Computing

Location

IBM Research Austin, TX USA

Bio

Dr. Andrew Cassidy is a member of the Brain Inspired Computing Group at IBM Research, the team that brought you the NorthPole Neural Inference processor and the TrueNorth neurosynaptic processor. His expertise is in non-traditional computer architecture, and brings experience in architectural optimization, design, and implementation, particularly for machine learning algorithms and neural network applications.

He has authored over 80 patents, 50 publications in international journals and conferences, and is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

Dr. Cassidy holds a Ph.D. in Electrical and Computer Engineering from Johns Hopkins University (2010) and a M.S. in Electrical and Computer Engineering from Carnegie Mellon University (2003).

Featured:

Dharmendra S. Modha et al. , "Neural inference at the frontier of energy, space, and time." Science 382 ,329-335 (2023).
https://www.science.org/doi/full/10.1126/science.adh1174

Publications

A low-power neurosynaptic implementation of Local Binary Patterns for texture analysis
- - Alexander Andreopoulos
  - Rodrigo Alvarez-Icaza
  - et al.
- 2016
- IJCNN 2016
Convolutional networks for fast, energy-efficient neuromorphic computing
- - Steven K. Esser
  - Paul Merolla
  - et al.
- 2016
- PNAS
Real-time sensory information processing using the TrueNorth Neurosynaptic System
- - Andreas G. Andreou
  - Andrew A. Dykman
  - et al.
- 2016
- ISCAS 2016
TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications
- - Jun Sawada
  - Filipp Akopyan
  - et al.
- 2016
- SC 2016
TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
- - Filipp Akopyan
  - Jun Sawada
  - et al.
- 2015
- IEEE TCADIS
Gibbs sampling with low-power spiking digital neurons
- - Srinjoy Das
  - Bruno U. Pedroni
  - et al.
- 2015
- ISCAS 2015
Visual saliency on networks of neurosynaptic cores
- - Alexander Andreopoulos
  - Brian Taba
  - et al.
- 2015
- IBM J. Res. Dev
A million spiking-neuron integrated circuit with a scalable communication network and interface
- - Paul A. Merolla
  - John V. Arthur
  - et al.
- 2014
- Science
Real-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ∼100× Speedup in Time-to-Solution and ∼100,000× Reduction in Energy-to-Solution
- - Andrew S. Cassidy
  - Rodrigo Alvarez-Icaza
  - et al.
- 2014
- SC 2014
Cognitive computing programming paradigm: A Corelet Language for composing networks of neurosynaptic cores
- - Arnon Amir
  - Pallab Datta
  - et al.
- 2013
- IJCNN 2013

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Patents

- 07 Mar 2022
- US
- 11270196
Multi-mode Low-precision Inner-product Computation Circuits For Massively Parallel Neural Inference Engine
- 28 Feb 2022
- US
- 11263011
Compound Instruction Set Architecture For A Neural Inference Chip
- 08 Feb 2022
- GB
- 2557780
Energy-efficient Time-multiplexed Neurosynaptic Core For Implementing Neural Networks Spanning Power- And Area-efficiency
- 31 Jan 2022
- US
- 11238347
Data Distribution In An Array Of Neural Network Cores
- 20 Dec 2021
- US
- 11205419
Low Energy Deep-learning Networks For Generating Auditory Features For Audio Processing Pipelines
- 22 Nov 2021
- US
- 11184221
Yield Tolerance In A Neurosynaptic System
- 26 Oct 2021
- GB
- 2590888
Data Representation For Dynamic Precision In Neural Network Cores
- 11 Oct 2021
- US
- 11144553
Streaming Programmable Point Mapper And Compute Hardware
- 10 Aug 2021
- GB
- 2586556
Time, Space, And Energy Efficient Neural Inference Via Parallelism And On-chip Memory
- 10 Aug 2021
- GB
- 2586763
A Central Scheduler And Instruction Dispatcher For A Neural Inference Processor

Top collaborators

Andrew Cassidy

Overview

Title

Location

Bio

Publications

A low-power neurosynaptic implementation of Local Binary Patterns for texture analysis

Convolutional networks for fast, energy-efficient neuromorphic computing

Real-time sensory information processing using the TrueNorth Neurosynaptic System

TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications

TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

Gibbs sampling with low-power spiking digital neurons

Visual saliency on networks of neurosynaptic cores

A million spiking-neuron integrated circuit with a scalable communication network and interface

Real-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ∼100× Speedup in Time-to-Solution and ∼100,000× Reduction in Energy-to-Solution

Cognitive computing programming paradigm: A Corelet Language for composing networks of neurosynaptic cores

Patents

Multi-mode Low-precision Inner-product Computation Circuits For Massively Parallel Neural Inference Engine

Compound Instruction Set Architecture For A Neural Inference Chip

Energy-efficient Time-multiplexed Neurosynaptic Core For Implementing Neural Networks Spanning Power- And Area-efficiency

Data Distribution In An Array Of Neural Network Cores

Low Energy Deep-learning Networks For Generating Auditory Features For Audio Processing Pipelines

Yield Tolerance In A Neurosynaptic System

Data Representation For Dynamic Precision In Neural Network Cores

Streaming Programmable Point Mapper And Compute Hardware

Time, Space, And Energy Efficient Neural Inference Via Parallelism And On-chip Memory

A Central Scheduler And Instruction Dispatcher For A Neural Inference Processor

Top collaborators

Alexander Andreopoulos

Dharmendra Modha

John Arthur

Guillaume Garreau