NeuroArch 2014

The first workshop on Neuromorphic Architectures (NeuroArch) aims at exploring novel ideas and research opportunities in design, programming, and application of neuromorphic and brain-inspired accelerators. In the current realm of processor design, where energy and power constraint has shifted the designs toward heterogeneity, hardware neural networks are emerging as candidate accelerators with attractive characteristics and broad application scope.

In addition to the power-efficiency and fault tolerance of neural accelerators, we are at the junction of time where:

1. As technology scales down to the atomic levels, the increasing process variability causes the designers to pay a high tax in performance and efficiency to provide fault-free designs; the intrinsic robustness of neural networks may lead to fault-tolerant accelerators.

2. Novel neural network algorithms such as Deep Belief Networks outperform many alternative machine learning algorithms across a broad set of applications.

3. Significant progress in neuroscience sheds light on the operating principles of biological neural networks, which can thus be partially replicated in hardware.

4. The landscape of computing has changed toward providing a more personalized and more targeted experience for the users, thus increasing the importance of applications that require learning.

Therefore, we believe it is imperative and timely for the computer architecture community and the design of next generation computing systems to explore and research neural models of computing.

To this end, NeuroArch invites research papers and talks on topics including but not limited to:

• Hardware design for biologically or mathematically inspired neural networks

• Applications of hardware neural networks

• Advanced technologies and devices for neural hardware design (3D, memristors, ...)

• Programming models and environments for neural accelerators

NeuroArch 2014 will include both invited talks and peer-reviewed papers. Peer-reviewed papers will not be published in a proceedings; therefore, submitting to NeuroArch will not preclude future publication opportunities. However, papers and presentation slides will be made available online with the authors' approval.

Important Dates

Paper Submission: April 1st, 2014
Author Notification: April 15th, 2014


Paper submissions are limited to two-page extended abstract. Please use the formatting guidelines from the main conference.

Please send a PDF version of your paper to and

Submissions may optionally be blind (authors can choose whether to include names on the submitted PDF; this option will be relevant if the organizing committee decides to query the opinion of an external reviewer).


Each talk consists of 25 min presentation and 5 min Q&A at the end.
• [9.00 AM - 9.30 AM] Programmable Smart Machines: A Hybrid Neuromorphic approach to General Purpose Computation - Jonathan Appavoo, Amos Waterland, Schuyler Eldridge, Kate Zhao, Ajay Joshi, Steve Homer and Margo Seltzer

• [9.30 AM - 10.00 AM] Building Neural Networks using NIDA Array Elements - Mark Dean, Catherine D. Schuman and J. Douglas Birdwell, University of Tennessee, Knoxville, TN

• [10.00 AM - 10.30 AM] Snack Break

• [10.30 AM - 11.00 AM] A Data-Reuse Aware Accelerator for Large-Scale Convolutional Networks - Maurice Peemen and Henk Corporaal, Eindhoven University of Technology, The Netherlands
• [11.00 AM - 11.30 AM] Neuron-like Hardware Architecture for Neuromorphic Computing - Byungik Ahn, KT, Seoul, Korea

• [11.30 AM - 12.00 AM] Computational Noise Resiliency in Deep Learning Architecture - Sek Chai, David Zhang and Gooitzen van der Wal , SRI International, Princeton, NJ

• [12.00 PM - 1.00 PM] Lunch Break
• [1.00 PM - 1.30 PM] A Modular, Distributed and Reconfigurable Processing Unit for Variable-Precision Neuromorphic Architectures - Ayose Falcon, Enric Herrero, Fernando Latorre, Pedro Lopez, Marc Lupon, Frederico Pratas, Georgios Tournavitis and Antonio Gonzalez, Intel Labs Barcelona
• [1.30 PM - 2.00 PM] Memristive Crossbar Based Neuromorphic Processors - Tarek M. Taha, Chris Yakopcic, Raqibul Hasan, Mark R. McLean and Doug Palmer


Daniel Ben Dayan-Rubin, Intel Labs, ICRI, Israel
Hadi Esmaeilzadeh, Georgia Tech
Abdullah Muzahid, University of Texas at San Antonio
Emre Neftci, UCSD
Olivier Temam, Inria

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