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Energy-Efficient Stochastic Computing with Superparamagnetic Tunnel Junctions
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DOI：10.1103/PhysRevApplied.13.034016.png)
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Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on precharge sense amplifiers. This generator is significantly more energy efficient than SMTJ-based bitstream generators that tune probabilities with spin currents and a factor of 2 more efficient than related CMOS-based implementations. The true randomness of this bitstream generator allows us to use them as the fundamental units of a novel neural network architecture. To take advantage of the potential savings, we codesign the algorithm with the circuit, rather than directly transcribing a classical neural network into hardware. The flexibility of the neural network mathematics allows us to adapt the network to the explicitly energy-efficient choices we make at the device level. The result is a convolutional neural network design operating at approximately 150 nJ per inference with 97% performance on the MNIST data set a factor of 1.4 to 7.7 improvement in energy efficiency over comparable proposals in the recent literature.
Keyword:
SPIN-TRANSFER TORQUE
NEURAL-NETWORKS
ARCHITECTURE
DESIGN
MODEL
期刊
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4.4
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7.1K
被引数:
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