Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks   The use of lower precision has emerged as a popular technique to optimize the
compute and storage requirements of complex Deep Neural Networks (DNNs). In the
quest for lower precision, recent studies have shown that ternary DNNs (which
represent weights and activations by signed ternary values) represent a
promising sweet spot, achieving accuracy close to full-precision networks on
complex tasks. We propose TiM-DNN, a programmable in-memory accelerator that is
specifically designed to execute ternary DNNs. TiM-DNN supports various ternary
representations including unweighted {-1,0,1}, symmetric weighted {-a,0,a}, and
asymmetric weighted {-a,0,b} ternary systems. The building blocks of TiM-DNN
are TiM tiles -- specialized memory arrays that perform massively parallel
signed ternary vector-matrix multiplications with a single access. TiM tiles
are in turn composed of Ternary Processing Cells (TPCs), bit-cells that
function as both ternary storage units and signed ternary multiplication units.
We evaluate an implementation of TiM-DNN in 32nm technology using an
architectural simulator calibrated with SPICE simulations and RTL synthesis. We
evaluate TiM-DNN across a suite of state-of-the-art DNN benchmarks including
both deep convolutional and recurrent neural networks. A 32-tile instance of
TiM-DNN achieves a peak performance of 114 TOPs/s, consumes 0.9W power, and
occupies 1.96mm2 chip area, representing a 300X and 388X improvement in TOPS/W
and TOPS/mm2, respectively, compared to an NVIDIA Tesla V100 GPU. In comparison
to specialized DNN accelerators, TiM-DNN achieves 55X-240X and 160X-291X
improvement in TOPS/W and TOPS/mm2, respectively. Finally, when compared to a
well-optimized near-memory accelerator for ternary DNNs, TiM-DNN demonstrates
3.9x-4.7x improvement in system-level energy and 3.2x-4.2x speedup,
underscoring the potential of in-memory computing for ternary DNNs.

3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas