Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We
  There Yet?   In-Memory Acceleration (IMA) promises major efficiency improvements in deep
neural network (DNN) inference, but challenges remain in the integration of IMA
within a digital system. We propose a heterogeneous architecture coupling 8
RISC-V cores with an IMA in a shared-memory cluster, analyzing the benefits and
trade-offs of in-memory computing on the realistic use case of a MobileNetV2
bottleneck layer. We explore several IMA integration strategies, analyzing
performance, area, and energy efficiency. We show that while pointwise layers
achieve significant speed-ups over software implementation, on depthwise layer
the inability to efficiently map parameters on the accelerator leads to a
significant trade-off between throughput and area. We propose a hybrid solution
where pointwise convolutions are executed on IMA while depthwise on the cluster
cores, achieving a speed-up of 3x over SW execution while saving 50% of area
when compared to an all-in IMA solution with similar performance.

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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
30
The categories of this paper are: q-alg
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The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-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
31
The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org