Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PISA: A Binary-Weight Processing-In-Sensor Accelerator for Edge Image
  Processing   This work proposes a Processing-In-Sensor Accelerator, namely PISA, as a
flexible, energy-efficient, and high-performance solution for real-time and
smart image processing in AI devices. PISA intrinsically implements a
coarse-grained convolution operation in Binarized-Weight Neural Networks
(BWNNs) leveraging a novel compute-pixel with non-volatile weight storage at
the sensor side. This remarkably reduces the power consumption of data
conversion and transmission to an off-chip processor. The design is completed
with a bit-wise near-sensor processing-in-DRAM computing unit to process the
remaining network layers. Once the object is detected, PISA switches to typical
sensing mode to capture the image for a fine-grained convolution using only the
near-sensor processing unit. Our circuit-to-application co-simulation results
on a BWNN acceleration demonstrate acceptable accuracy on various image
datasets in coarse-grained evaluation compared to baseline BWNN models, while
PISA achieves a frame rate of 1000 and efficiency of ~1.74 TOp/s/W. Lastly,
PISA substantially reduces data conversion and transmission energy by ~84%
compared to a baseline CPU-sensor design.

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The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom