Neuromorphic Co-Design as a Game

no code implementations • 11 Dec 2023 • Craig M. Vineyard, William M. Severa, James B. Aimone

In particular, we consider the interplay between algorithm and architecture advances in the field of neuromorphic computing.

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation • 10 Apr 2023 • Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Weijie Ke, Mina A Khoei, Denis Kleyko, Noah Pacik-Nelson, Alessandro Pierro, Philipp Stratmann, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Shih-Chii Liu, Yao-Hong Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan R. Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Kenneth Stewart, Matthew Stewart, Terrence C. Stewart, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems.

Benchmarking

Spiking Neural Streaming Binary Arithmetic

no code implementations • 23 Mar 2022 • James B. Aimone, Aaron J. Hill, William M. Severa, Craig M. Vineyard

Boolean functions and binary arithmetic operations are central to standard computing paradigms.

RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search

no code implementations • 8 Nov 2019 • Sam Green, Craig M. Vineyard, Ryan Helinski, Çetin Kaya Koç

Gradient-based approaches impose more structure on the search, compared to alternative NAS methods, enabling faster search phase optimization.

Neural Architecture Search

Composing Neural Algorithms with Fugu

no code implementations • 28 May 2019 • James B. Aimone, William Severa, Craig M. Vineyard

Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources.

Distillation Strategies for Proximal Policy Optimization

no code implementations • 23 Jan 2019 • Sam Green, Craig M. Vineyard, Çetin Kaya Koç

DQN distillation extended the original distillation idea to transfer information stored in a high performance, high capacity teacher Q-function trained via the Deep Q-Learning (DQN) algorithm.

Deep Reinforcement Learning Q-Learning +1

Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication

no code implementations • 26 Oct 2018 • William Severa, Craig M. Vineyard, Ryan Dellana, Stephen J. Verzi, James B. Aimone

We present a method for training deep spiking neural networks using an iterative modification of the backpropagation optimization algorithm.

General Classification Image Classification +1

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

no code implementations • 21 Mar 2017 • Michael R. Smith, Aaron J. Hill, Kristofor D. Carlson, Craig M. Vineyard, Jonathon Donaldson, David R. Follett, Pamela L. Follett, John H. Naegle, Conrad D. James, James B. Aimone

Information in neural networks is represented as weighted connections, or synapses, between neurons.

speech-recognition Speech Recognition

Neurogenesis Deep Learning

no code implementations • 12 Dec 2016 • Timothy J. Draelos, Nadine E. Miner, Christopher C. Lamb, Jonathan A. Cox, Craig M. Vineyard, Kristofor D. Carlson, William M. Severa, Conrad D. James, James B. Aimone

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks.

BIG-bench Machine Learning Deep Learning +1