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Filipp Akopyan,
Jun Sawada, Andrew Cassidy, Rodrigo Alvarez-Icaza,
John Arthur,
Paul Merolla,
Nabil Imam,
Y. Nakamura,
P. Datta,
Gi-Joon Nam,
Brian Taba,
M. Beakes,
B. Brezzo,
J.B. Kuang,
Rajit Manohar,
William Risk,
Bryan Jackson,
Dharmendra Modha
The new era of cognitive computing brings forth the grand challenge of
developing systems capable of processing massive amounts of noisy
multisensory data. This type of intelligent computing poses a set of
constraints, including real-time operation, low-power consumption and
scalability, which require a radical departure from conventional
system design. Brain-inspired architectures offer tremendous promise
in this area. To this end, we developed TrueNorth, a 65 mW real-time
neurosynaptic processor that implements a non-von Neumann, low-power,
highly-parallel, scalable, and defect-tolerant architecture. With 4096
neurosynaptic cores, the TrueNorth chip contains 1 million digital
neurons and 256 million synapses tightly interconnected by an
event-driven routing infrastructure. The fully digital 5.4 billion
transistor implementation leverages existing CMOS scaling trends,
while ensuring one-to-one correspondence between hardware and
software. With such aggressive design metrics and the TrueNorth
architecture breaking path with prevailing architectures, it is clear
that conventional computer-aided design (CAD) tools could not be used
for the design. As a result, we developed a novel design methodology
that includes mixed asynchronous-synchronous circuits and a complete
tool flow for building an event-driven, low-power neurosynaptic
chip. The TrueNorth chip is fully configurable in terms of
connectivity and neural parameters to allow custom configurations for
a wide range of cognitive and sensory perception applications. To
reduce the system's communication energy, we have adapted existing
application-agnostic very large-scale integration CAD placement tools
for mapping logical neural networks to the physical neurosynaptic core
locations on the TrueNorth chips. With that, we have successfully
demonstrated the use of TrueNorth-based systems in multiple
applications, including visual object recognition, with higher
performance and orders of magnitude lower power consumpt- on than the
same algorithms run on von Neumann architectures. The TrueNorth chip
and its tool flow serve as building blocks for future cognitive
systems, and give designers an opportunity to develop novel
brain-inspired architectures and systems based on the knowledge
obtained from this paper.
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