Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Google Neural Network Models for Edge Devices: Analyzing and Mitigating
  Machine Learning Inference Bottlenecks   Emerging edge computing platforms often contain machine learning (ML)
accelerators that can accelerate inference for a wide range of neural network
(NN) models. These models are designed to fit within the limited area and
energy constraints of the edge computing platforms, each targeting various
applications (e.g., face detection, speech recognition, translation, image
captioning, video analytics). To understand how edge ML accelerators perform,
we characterize the performance of a commercial Google Edge TPU, using 24
Google edge NN models (which span a wide range of NN model types) and analyzing
each NN layer within each model. We find that the Edge TPU suffers from three
major shortcomings: (1) it operates significantly below peak computational
throughput, (2) it operates significantly below its theoretical energy
efficiency, and (3) its memory system is a large energy and performance
bottleneck. Our characterization reveals that the one-size-fits-all, monolithic
design of the Edge TPU ignores the high degree of heterogeneity both across
different NN models and across different NN layers within the same NN model,
leading to the shortcomings we observe.
  We propose a new acceleration framework called Mensa. Mensa incorporates
multiple heterogeneous edge ML accelerators (including both on-chip and
near-data accelerators), each of which caters to the characteristics of a
particular subset of NN models and layers. During NN inference, for each NN
layer, Mensa decides which accelerator to schedule the layer on, taking into
account both the optimality of each accelerator for the layer and
layer-to-layer communication costs. Averaged across all 24 Google edge NN
models, Mensa improves energy efficiency and throughput by 3.0x and 3.1x over
the Edge TPU, and by 2.4x and 4.3x over Eyeriss~v2, a state-of-the-art
accelerator.

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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an