Neuroscience and Computing

	TrueNorth, 1M neurons
	and 256M synapses

The architecture of modern computer systems is quite different from what we know about the organization of the brain. Most silicon circuits use a global clock to coordinate activities while the brain operates in an asynchronous, event-driven manner. There is a clear separation between memory and computation circuits in conventional computers, while memory and computation appear to be tightly integrated in brains. The fan-out of a compute element in CMOS is small relative to the thousands of connections made by an individual neuron to others. The goal of this research direction is to extract computational principles from neuroscience and apply them to silicon-based computation.

One principle seems to be the notion of sending information only when there is a divergence from expected behavior. To capture this notion, we propose a computation structure known as a Δ-dataflow network. Δ-dataflow networks have filtering built-into their computation structure. They can be used to implement a wide variety of operations in an energy efficient manner, and are suited for implementation on an asynchronous architecture.

A second approach we are investigating is in the realm of spiking computation. The neuromorphic VLSI community has been using asynchronous circuits and address-event representation to build systems that mimic biological neurons. Questions that are of interest include: What are the applications of such systems? How can we compute using a spike-based representation? How do we design scalable neuromorphic systems that are highly energy-efficient? How do biological neurons "compute"?

Finally, we are working with researchers in medicine and neurosurgery on architectures for interfacing electronics with the brain.

Collaborators

Brain-computer interfaces

• Abhishek Bhattacharjee (CS)
• Dr. Dennis Spencer (Yale School of Medicine)
• Hitten Zaveri (Yale School of Medicine)

The Brainstorm project, with

• Kwabena Boahen (Stanford)
• Chris Eliasmith (U. Waterloo)

The SyNAPSE project, with

• Dharmendra Modha (IBM)

Participants

Jakob Jordan

Congyang Li

Xiayuan Wen

Alums

Filipp Akopyan (Ph.D. 2011)

Nabil Imam (Ph.D. 2014)

Ioannis Karageorgos (Ph.D. 2017 KU Leuven)

Prafull Purohit (Ph.D. 2023)

Saber Moradi (Ph.D. 2013 ETH Zurich)

Publications

Raghavendra Pothukuchi, Karthik Sriram, Michal Gerasimiuk, Muhammed Ugur, Rajit Manohar, Anurag Khandelwal, and Abhishek Bhattacharjee. Distributed Brain-Computer Interfacing with a Networked Multi-Accelerator Architecture. IEEE Micro (special issue on Top Picks from Computer Architecture conferences), 2024.

Prafull Purohit, Johannes Leugering, and Rajit Manohar. An Efficient Data Structure for Sparse Bit-Vectors with Applications in Neuromorphic Computing. IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), July 2023. (abstract, pdf)

Karthik Sriram, Raghavendra Pothukuchi, Michal Gerasimuk, Muhammed Ugur, Oliver Ye Rajit Manohar Anurag Khandelwal, and Abhishek Bhattacharjee. SCALO: An Accelerator-Rich Distributed System for Scalable Brain-Computer Interfacing . IEEE/ACM International Symposium on Computer Architecture (ISCA), July 2023. (abstract, pdf)   — Best paper award   — IEEE Micro Top Picks

Abhishek Bhattacharjee, Rajit Manohar, and Karthik Sriram,. RETROSPECTIVE: Hardware-software co-design for Brain-Computer Interfaces. ISCA@50 Retrospective, June 2023.    — ISCA-50 25-year retrospective

Ioannis Karageorgos, Karthik Sriran, Xiayuan Wen, Jan Vesely, Nick Lindsay, Michael Wu, Lenny Kazan, Raghavendra Pothukuchi, Rajit Manohar, and Abhishek Bhattacharjee. HALO: A Hardware-Software Co-Designed Processor for Brain-Computer Interfaces. IEEE Micro, Special issue from the HotChips 2022 conference, 2023.

Prafull Purohit and Rajit Manohar. Field-programmable encoding for address-event representation. Frontiers in Neuroscience, 16, December 2022. (pdf)

Ioannis Karageorgos, Karthik Sriran, Jan Vesely, Michael Wu, Xiayuan Wen, Nick Lindsay, Lenny Kazan, Rajit Manohar, Abhishek Bhattacharjee. HALO: A Flexible and Low Power Processing Fabric for Brain-Computer Interfaces. HotChips 2022: Workshop on High-Performance Chips, August 2022. (pdf)

Rajit Manohar. Hardware/software co-design for Neuromorphic Systems. Proceedings of the IEEE Custom Integrated Circuits Conference [invited] (CICC), April 2022. (pdf)

Prafull Purohit and Rajit Manohar. Hierarchical Token Rings for Address-Event Encoding. IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), September 2021. (abstract, pdf)

Karthik Sriram, Ioannis Karageorgos, Jan Vesely, Nick Lindsay, Xiayuan Wen, Michael Wu, Marc Powell, David Borton, Rajit Manohar, Abhishek Bhattacharjee. Balancing Specialized Versus Flexible Computation in Brain-Computer Interfaces. IEEE Micro (special issue on Top Picks from Computer Architecture conferences), 2021. (pdf)

Ioannis Karageorgos, Karthik Sriram, Jan Vesely, Michael Wu, Marc Powell, David Borton, Rajit Manohar, and Abhishek Bhattacharjee. Hardware-software co-design for Brain-Computer Interfaces. Proceedings of the IEEE/ACM Symposium on Computer Architecture (ISCA), June 2020. (abstract, pdf)   — IEEE Micro Top Picks

Alexander Neckar, Sam Fok, Ben Benjamin, Terrence C. Stewart, Nick N. Oza, Aaron R. Voelker, Chris Eliasmith, Rajit Manohar, Kwabena Boahen. Braindrop: A Mixed-Signal Neuromorphic Architecture with a Dynamical Systems-Based Programming Model. Proceeedings of the IEEE, 107(1):144--164, January 2019. (abstract, pdf)

Saber Moradi and Rajit Manohar. The Impact of On-chip Communication on Memory Technologies for Neuromorphic Systems. Journal of Physics D: Applied Physics, 52(1), Special issue on brain-inspired pervasive computing: from materials to neuromorphic architectures/applications, October 2018. (abstract, pdf)

Saber Moradi, Sunil Bhave, and Rajit Manohar. Energy-efficient Hybrid CMOS-NEMS LIF Neuron Circuit in 28nm CMOS. IEEE Symposium Series on Computational Intelligence, November 2017. (abstract, pdf)

Tayyar Rzayev, Saber Moradi, David Albonesi, and Rajit Manohar. DeepRecon: Dynamically Reconfigurable Architecture for Accelerating Deep Neural Networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN), May 2017. (abstract, pdf)

Tayyar Rzayev, Saber Moradi, David Albonesi, and Rajit Manohar. Fractured Arithmetic Accelerator for Training Deep Neural Networks. Workshop on Hardware and Algorithms for On-chip Learning, International Conference on Computer-Aided Design (ICCAD), November 2016.

Filipp Akopyan, Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza, John Arthur, Paul Merolla, Nabil Imam, Yutaka Nakamura, Pallab Datta, Gi-Joon Nam, Brian Taba, Michael Beakes, Bernard Brezzo, Jente Kuang, Rajit Manohar, William Risk, Bryan Jackson, and Dharmendra Modha. TrueNorth: Design and Tool Flow of a 65mW 1 Million Neuron Programmable Neurosynaptic Chip. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 34(10) (TCAD), October 2015. (abstract, pdf)   — Keynote paper

Giovanni Rovere, Nabil Imam, Rajit Manohar, and Chiara Bartolozzi. A QDI Asynchronous AER Serializer/Deserializer Link in 180nm for Event-Based Sensors for Robotic Applications. Proceedings of the International Symposium on Circuits and Systems, May 2015. (abstract, pdf)

Andrew S. Cassidy, Rodrigo Alvarez-Icaza, Filipp Akopyan, Jun Sawada, John V. Arthur, Paul A. Merolla, Pallab Datta, Marc Gonzalez Tallada, Brian Taba, Alexander Andreopoulos, Arnon Amir, Steven K. Esser, Jeff Kusnitz, Rathinakumar Appuswamy, Chuck Haymes, Bernard Brezzo, Roger Moussalli, Ralph Bellofatto, Christian Baks, Michael Mastro, Kai Schleupen, Charles E. Cox, Ken Inoue, Steve Millman, Nabil Imam, Emmett McQuinn, Yutaka T. Nakamura, Ivan Vo, Chen Guo, Don Nguyen, Scott Lekuch, Sameh Assad, Daniel Friedman, Bryan L. Jackson, Myron D. Flickner, William P. Risk, Rajit Manohar, Dharmendra S. Modha. Real-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ~100x Speedup in Time-to-Solution and ~100,000x Reduction in Energy-to-Solution. Proceedings of Supercomputing 2014, November 2014. (abstract, pdf)   — ACM Gordon Bell Prize finalist

Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza, Andrew S. Cassidy, Jun Sawada, Filipp Akopyan, Bryan L. Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, Bernad Brezzo, Ivan Vo, Steven K. Esser, Rathinakumar Appuswamy, Brian Taba, Arnon Amir, Myron D. Flickner, William P. Risk, Rajit Manohar, and Dharmendra Modha. A Million Spiking-Neuron Integrated Circuit with a Scalable Communication Network and Interface. Science, 345(6197):668--673, August 2014. (abstract, pdf)   — IBM Research 2014 Pat Goldberg Math/CS/EE Best Paper Award

Saber Moradi, Nabil Imam, Rajit Manohar, and Giacomo Indiveri. A Memory-Efficient Routing Method for Large-Scale Spiking Neural Networks. 21st European Conference on Circuit Theory and Design, September 2013. (abstract, pdf)

Nabil Imam, Kyle Wecker, Jonathan Tse, Robert Karmazin, and Rajit Manohar. Neural Spiking Dynamics in Asynchronous Digital Circuits. 2013 International Joint Conference on Neural Networks (IJCNN), August 2013. (abstract, pdf)

Rajit Manohar. Scalable Routing in Large-Scale Neuromorphic Systems. Symposium on Large-Scale Neuromorphic Systems at the Annual International Conference of the IEEE Engineering in Medicine and Biology Society [invited], August 2012.

John Arthur, Paul Merolla, Filipp Akopyan, Rodrigo Alvarez, Andrew Cassidy, Shyamal Chandra, Steven Esser, Nabil Imam, William Risk, Daniel Rubin, Rajit Manohar and Dharmendra Modha. Building Block of a Programmable Neuromorphic Substrate: A Digital Neurosynaptic Core. 2012 International Joint Conference on Neural Networks (IJCNN), June 2012. (abstract, pdf)

Nabil Imam, Thomas Cleland, Rajit Manohar, Paul Merolla, John Arthur, Filipp Akopyan, and Dharmendra Modha. Implementation of Olfactory Bulb Glomerular Layer Computation in a Digital Neurosynaptic Core. Frontiers of Neuromorphic Engineering, Vol. 6, Number 83, June 2012. (abstract, pdf)

Nabil Imam, Filipp Akopyan, Paul Merolla, John Arthur, Rajit Manohar, and Dharmendra Modha. A Digital Neurosynaptic Core Using Event-Driven QDI Circuits. Proceedings of the 18th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), May 2012. (abstract, pdf)   — Best paper award

Paul Merolla, John Arthur, Filipp Akopyan, Nabil Imam, Rajit Manohar, Dharmendra Modha. A Digital Neurosynaptic Core Using Embedded Crossbar Memory with 45pJ per Spike in 45nm. Proceedings of the IEEE Custom Integrated Circuits Conference (CICC), September 2011. (abstract, pdf)

Nabil Imam and Rajit Manohar. Address-Event Communication Using Token-Ring Mutual Exclusion. Proceedings of the 17th IEEE International Symposium on Asynchronous Circuits and Systems (ASYNC), April 2011. (abstract, pdf)

Jon Russo, Mohammed Amduka, Keith Pendersen, Richard Lethin, Jonathan Springer, Rajit Manohar, Rami Melhem. Enabling Cognitive Architectures for UAV Mission Planning. Proceedings of the High Performance Embedded Computing Workshop (HPEC), September 2006. (abstract, pdf)   — Best paper award

Rajit Manohar and K. Mani Chandy. Δ-Dataflow Networks for Event Stream Processing. Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems, November 2004. (abstract, pdf, ps)   — Best paper award