72 research outputs found
Spin-Based Neuron Model with Domain Wall Magnets as Synapse
We present artificial neural network design using spin devices that achieves
ultra low voltage operation, low power consumption, high speed, and high
integration density. We employ spin torque switched nano-magnets for modelling
neuron and domain wall magnets for compact, programmable synapses. The spin
based neuron-synapse units operate locally at ultra low supply voltage of 30mV
resulting in low computation power. CMOS based inter-neuron communication is
employed to realize network-level functionality. We corroborate circuit
operation with physics based models developed for the spin devices. Simulation
results for character recognition as a benchmark application shows 95% lower
power consumption as compared to 45nm CMOS design
Boolean and Non-Boolean Computation With Spin Devices
Recently several device and circuit design techniques have been explored for
applying nano-magnets and spin torque devices like spin valves and domain wall
magnets in computational hardware. However, most of them have been focused on
digital logic, and, their benefits over robust and high performance CMOS
remains debatable. Ultra-low voltage, current-switching operation of
magneto-metallic spin torque devices can potentially be more suitable for
non-Boolean computation schemes that can exploit current-mode analog
processing. Device circuit co-design for different classes of
non-Boolean-architectures using spin-torque based neuron models in spin-CMOS
hybrid circuits show that the spin-based non-Boolean designs can achieve
15X-100X lower computation energy for applications like, image-processing,
data-conversion, cognitive-computing, pattern matching and programmable-logic,
as compared to state of art CMOS designs.Comment: arXiv admin note: substantial text overlap with arXiv:1206.322
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