Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MC-CIM: Compute-in-Memory with Monte-Carlo Dropouts for Bayesian Edge
  Intelligence   We propose MC-CIM, a compute-in-memory (CIM) framework for robust, yet low
power, Bayesian edge intelligence. Deep neural networks (DNN) with
deterministic weights cannot express their prediction uncertainties, thereby
pose critical risks for applications where the consequences of mispredictions
are fatal such as surgical robotics. To address this limitation, Bayesian
inference of a DNN has gained attention. Using Bayesian inference, not only the
prediction itself, but the prediction confidence can also be extracted for
planning risk-aware actions. However, Bayesian inference of a DNN is
computationally expensive, ill-suited for real-time and/or edge deployment. An
approximation to Bayesian DNN using Monte Carlo Dropout (MC-Dropout) has shown
high robustness along with low computational complexity. Enhancing the
computational efficiency of the method, we discuss a novel CIM module that can
perform in-memory probabilistic dropout in addition to in-memory weight-input
scalar product to support the method. We also propose a compute-reuse
reformulation of MC-Dropout where each successive instance can utilize the
product-sum computations from the previous iteration. Even more, we discuss how
the random instances can be optimally ordered to minimize the overall
MC-Dropout workload by exploiting combinatorial optimization methods.
Application of the proposed CIM-based MC-Dropout execution is discussed for
MNIST character recognition and visual odometry (VO) of autonomous drones. The
framework reliably gives prediction confidence amidst non-idealities imposed by
MC-CIM to a good extent. Proposed MC-CIM with 16x31 SRAM array, 0.85 V supply,
16nm low-standby power (LSTP) technology consumes 27.8 pJ for 30 MC-Dropout
instances of probabilistic inference in its most optimal computing and
peripheral configuration, saving 43% energy compared to typical execution.

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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex