Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High-Accuracy Inference in Neuromorphic Circuits using Hardware-Aware
  Training   Neuromorphic Multiply-And-Accumulate (MAC) circuits utilizing synaptic weight
elements based on SRAM or novel Non-Volatile Memories (NVMs) provide a
promising approach for highly efficient hardware representations of neural
networks. NVM density and robustness requirements suggest that off-line
training is the right choice for "edge" devices, since the requirements for
synapse precision are much less stringent. However, off-line training using
ideal mathematical weights and activations can result in significant loss of
inference accuracy when applied to non-ideal hardware. Non-idealities such as
multi-bit quantization of weights and activations, non-linearity of weights,
finite max/min ratios of NVM elements, and asymmetry of positive and negative
weight components all result in degraded inference accuracy. In this work, it
is demonstrated that non-ideal Multi-Layer Perceptron (MLP) architectures using
low bitwidth weights and activations can be trained with negligible loss of
inference accuracy relative to their Floating Point-trained counterparts using
a proposed off-line, continuously differentiable HW-aware training algorithm.
The proposed algorithm is applicable to a wide range of hardware models, and
uses only standard neural network training methods. The algorithm is
demonstrated on the MNIST and EMNIST datasets, using standard MLPs.

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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
2
The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: mtrl-th