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Dharmendra S. Modha

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Distinguished Alumnus Award

March 18, 2015 By dmodha

On March 10, 2015, at the 56th Foundation Day of IIT Bombay, I was selected for Distinguished Alumnus Award. I am grateful for the education that I received at IIT Bombay, for my teachers, for my fellow students, for my hostel mates, for the mess workers who fed me for four years, for the support staff, for my colleagues at IBM, and, of course, my family. Of the nearly 50,000 alumni, to date, roughly 100 have been honored. Previous Awardees include Nandan Nilekani and Kanwal Rekhi as well as two IBM Fellows Subramanian Iyer and Ramesh Agarwal.

IIT Bombay
Photo Credit: Hita Bambhania-Modha

Filed Under: Accomplishments, Prizes

IBM Brings the Brain Chip to Capitol Hill

February 26, 2015 By dmodha

For details, see here.

Capitol Hill

Filed Under: Accomplishments, Brain-inspired Computing, Presentations

Brain-inspired Computing: A Decade-in-Review (2004-2014)

December 18, 2014 By dmodha

As 2014 comes to a close, TrueNorth (see here and here) was named a Runners-up for 2014 Breakthrough of the Year by Science Magazine.

This video and the following timeline provide a retrospective from 2004 to 2014 on the evolution of Brain-inspired Computing.

SyNAPSE Timeline
Illustration Credit: William Risk

Filed Under: Accomplishments, Brain-inspired Computing, Prizes

Panel: Beyond Von Neumann, Neuromophic Systems and Architectures

November 21, 2014 By dmodha

On November 21, 2014, at Supercomputing 2014, I participated in a panel on “Beyond Von Neumann, Neuromophic Systems and Architectures” organized by Mark E. Dean along with R. Jacob Vogelstein, Karlheinz Meier, Kris Carlson, and Dhireesha Kudithipudi.

Filed Under: Accomplishments, Brain-inspired Computing, Presentations

Supercomputing 2014

November 20, 2014 By dmodha

This week, IBM and Cornell team presented a ACM Gordon Bell Prize Finalist paper at Supercomputing 2014:

Title: “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”.

Authors: 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

Abstract:
Drawing on neuroscience, we have developed a parallel, event-driven kernel for neurosynaptic computation, that is efficient with respect to computation, memory, and communication. Building on the previously demonstrated highly-optimized software expression of the kernel, here, we demonstrate TrueNorth, a co-designed silicon expression of the kernel. TrueNorth achieves five orders of magnitude reduction in energy-to-solution and two orders of magnitude speedup in time-to-solution, when running computer vision applications and complex recurrent neural network simulations. Breaking path with the von Neumann architecture, TrueNorth is a 4,096 core, 1 million neuron, and 256 million synapse brain-inspired neurosynaptic processor, that consumes 65mW of power running at real-time and delivers performance of 46 Giga-Synaptic OPS/Watt. We demonstrate seamless tiling of TrueNorth chips into arrays, forming a foundation for cortex-like scalability. Unprecedented time-to-solution, energy-to-solution, scale, and performance of TrueNorth, combined with underlying flexibility of the kernel enables a broad range of cognitive applications.

The long-term aspiration is to build a “1%-human-scale” system with 4,096 processors one trillion synapses and that consumes merely 4kW.

4096 Processor System
Illustration Credit: William Risk

Filed Under: Accomplishments, Brain-inspired Computing, Papers

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