Pruning for Improved ADC Efficiency in Crossbar-based Analog In-memory
  Accelerators
Timur Ibrayev Isha Garg Indranil Chakraborty Kaushik Roy
Deep learning has proved successful in many applications but suffers from
high computational demands and requires custom accelerators for deployment.
Crossbar-based analog in-memory architectures are attractive for acceleration
of deep neural networks (DNN), due to their high data reuse and high efficiency
enabled by combining storage and computation in memory. However, they require
analog-to-digital converters (ADCs) to communicate crossbar outputs. ADCs
consume a significant portion of energy and area of every crossbar processing
unit, thus diminishing the potential efficiency benefits. Pruning is a
well-studied technique to improve the efficiency of DNNs but requires
modifications to be effective for crossbars. In this paper, we motivate
crossbar-attuned pruning to target ADC-specific inefficiencies. This is
achieved by identifying three key properties (dubbed D.U.B.) that induce
sparsity that can be utilized to reduce ADC energy without sacrificing
accuracy. The first property ensures that sparsity translates effectively to
hardware efficiency by restricting sparsity levels to Discrete powers of 2. The
other 2 properties encourage columns in the same crossbar to achieve both
Unstructured and Balanced sparsity in order to amortize the accuracy drop. The
desired D.U.B. sparsity is then achieved by regularizing the variance of
$L_{0}$ norms of neighboring columns within the same crossbar. Our proposed
implementation allows it to be directly used in end-to-end gradient-based
training. We apply the proposed algorithm to convolutional layers of VGG11 and
ResNet18 models, trained on CIFAR-10 and ImageNet datasets, and achieve up to
7.13x and 1.27x improvement, respectively, in ADC energy with less than 1% drop
in accuracy.