Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error
  Resilience for Energy Efficient Deep Neural Network Accelerators   Hardware accelerators are being increasingly deployed to boost the
performance and energy efficiency of deep neural network (DNN) inference. In
this paper we propose Thundervolt, a new framework that enables aggressive
voltage underscaling of high-performance DNN accelerators without compromising
classification accuracy even in the presence of high timing error rates. Using
post-synthesis timing simulations of a DNN accelerator modeled on the Google
TPU, we show that Thundervolt enables between 34%-57% energy savings on
state-of-the-art speech and image recognition benchmarks with less than 1% loss
in classification accuracy and no performance loss. Further, we show that
Thundervolt is synergistic with and can further increase the energy efficiency
of commonly used run-time DNN pruning techniques like Zero-Skip.

28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin