Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SparseNN: An Energy-Efficient Neural Network Accelerator Exploiting
  Input and Output Sparsity   Contemporary Deep Neural Network (DNN) contains millions of synaptic
connections with tens to hundreds of layers. The large computation and memory
requirements pose a challenge to the hardware design. In this work, we leverage
the intrinsic activation sparsity of DNN to substantially reduce the execution
cycles and the energy consumption. An end-to-end training algorithm is proposed
to develop a lightweight run-time predictor for the output activation sparsity
on the fly. From our experimental results, the computation overhead of the
prediction phase can be reduced to less than 5% of the original feedforward
phase with negligible accuracy loss. Furthermore, an energy-efficient hardware
architecture, SparseNN, is proposed to exploit both the input and output
sparsity. SparseNN is a scalable architecture with distributed memories and
processing elements connected through a dedicated on-chip network. Compared
with the state-of-the-art accelerators which only exploit the input sparsity,
SparseNN can achieve a 10%-70% improvement in throughput and a power reduction
of around 50%.

16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin