Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance

In recent years, researchers have focused on reducing the model size and number of computations (measured as "multiply-accumulate" or MAC operations) of DNNs. The energy consumption of a DNN depends on both the number of MAC operations and the energy efficiency of each MAC operation. The former can be estimated at design time; however, the latter depends on the intricate data reuse patterns and underlying hardware architecture. Hence, estimating it at design time is challenging. This work shows that the conventional approach to estimate the data reuse, viz. arithmetic intensity, does not always correctly estimate the degree of data reuse in DNNs since it gives equal importance to all the data types. We propose a novel model, termed "data type aware weighted arithmetic intensity" ($DI$), which accounts for the unequal importance of different data types in DNNs. We evaluate our model on 25 state-of-the-art DNNs on two GPUs. We show that our model accurately models data-reuse for all possible data reuse patterns for different types of convolution and different types of layers. We show that our model is a better indicator of the energy efficiency of DNNs. We also show its generality using the central limit theorem.Comment: Accepted at IEEE Transactions on Computers (Special Issue on Machine-Learning Architectures and Accelerators) 202

DeepReShape: Redesigning Neural Networks for Efficient Private Inference

Prior work on Private Inference (PI)--inferences performed directly on encrypted input--has focused on minimizing a network's ReLUs, which have been assumed to dominate PI latency rather than FLOPs. Recent work has shown that FLOPs for PI can no longer be ignored and have high latency penalties. In this paper, we develop DeepReShape, a network redesign technique that tailors architectures to PI constraints, optimizing for both ReLUs and FLOPs for the first time. The {\em key insight} is that a strategic allocation of channels such that the network's ReLUs are aligned in their criticality order simultaneously optimizes ReLU and FLOPs efficiency. DeepReShape automates network development with an efficient process, and we call generated networks HybReNets. We evaluate DeepReShape using standard PI benchmarks and demonstrate a 2.1\% accuracy gain with a 5.2$\times$ runtime improvement at iso-ReLU on CIFAR-100 and an 8.7$\times$ runtime improvement at iso-accuracy on TinyImageNet. Furthermore, we demystify the input network selection in prior ReLU optimizations and shed light on the key network attributes enabling PI efficiency.Comment: 37 pages, 23 Figures, and 17 Table

E2GC: Energy-efficient Group Convolution in Deep Neural Networks

The number of groups ($g$) in group convolution (GConv) is selected to boost the predictive performance of deep neural networks (DNNs) in a compute and parameter efficient manner. However, we show that naive selection of $g$ in GConv creates an imbalance between the computational complexity and degree of data reuse, which leads to suboptimal energy efficiency in DNNs. We devise an optimum group size model, which enables a balance between computational cost and data movement cost, thus, optimize the energy-efficiency of DNNs. Based on the insights from this model, we propose an "energy-efficient group convolution" (E2GC) module where, unlike the previous implementations of GConv, the group size ($G$) remains constant. Further, to demonstrate the efficacy of the E2GC module, we incorporate this module in the design of MobileNet-V1 and ResNeXt-50 and perform experiments on two GPUs, P100 and P4000. We show that, at comparable computational complexity, DNNs with constant group size (E2GC) are more energy-efficient than DNNs with a fixed number of groups (F$g$GC). For example, on P100 GPU, the energy-efficiency of MobileNet-V1 and ResNeXt-50 is increased by 10.8% and 4.73% (respectively) when E2GC modules substitute the F$g$GC modules in both the DNNs. Furthermore, through our extensive experimentation with ImageNet-1K and Food-101 image classification datasets, we show that the E2GC module enables a trade-off between generalization ability and representational power of DNN. Thus, the predictive performance of DNNs can be optimized by selecting an appropriate $G$. The code and trained models are available at https://github.com/iithcandle/E2GC-release.Comment: Accepted as a conference paper in 2020 33rd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID

Characterizing and Optimizing End-to-End Systems for Private Inference

Increasing privacy concerns have given rise to Private Inference (PI). In PI, both the client's personal data and the service provider's trained model are kept confidential. State-of-the-art PI protocols combine several cryptographic primitives: Homomorphic Encryption (HE), Secret Sharing (SS), Garbled Circuits (GC), and Oblivious Transfer (OT). Today, PI remains largely arcane and too slow for practical use, despite the need and recent performance improvements. This paper addresses PI's shortcomings with a detailed characterization of a standard high-performance protocol to build foundational knowledge and intuition in the systems community. The characterization pinpoints all sources of inefficiency -- compute, communication, and storage. A notable aspect of this work is the use of inference request arrival rates rather than studying individual inferences in isolation. Prior to this work, and without considering arrival rate, it has been assumed that PI pre-computations can be handled offline and their overheads ignored. We show this is not the case. The offline costs in PI are so high that they are often incurred online, as there is insufficient downtime to hide pre-compute latency. We further propose three optimizations to address the computation (layer-parallel HE), communication (wireless slot allocation), and storage (Client-Garbler) overheads leveraging insights from our characterization. Compared to the state-of-the-art PI protocol, the optimizations provide a total PI speedup of 1.8$\times$, with the ability to sustain inference requests up to a 2.24$\times$ greater rate.Comment: 12 figure

ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks

The capability of the self-attention mechanism to model the long-range dependencies has catapulted its deployment in vision models. Unlike convolution operators, self-attention offers infinite receptive field and enables compute-efficient modeling of global dependencies. However, the existing state-of-the-art attention mechanisms incur high compute and/or parameter overheads, and hence unfit for compact convolutional neural networks (CNNs). In this work, we propose a simple yet effective "Ultra-Lightweight Subspace Attention Mechanism" (ULSAM), which infers different attention maps for each feature map subspace. We argue that leaning separate attention maps for each feature subspace enables multi-scale and multi-frequency feature representation, which is more desirable for fine-grained image classification. Our method of subspace attention is orthogonal and complementary to the existing state-of-the-arts attention mechanisms used in vision models. ULSAM is end-to-end trainable and can be deployed as a plug-and-play module in the pre-existing compact CNNs. Notably, our work is the first attempt that uses a subspace attention mechanism to increase the efficiency of compact CNNs. To show the efficacy of ULSAM, we perform experiments with MobileNet-V1 and MobileNet-V2 as backbone architectures on ImageNet-1K and three fine-grained image classification datasets. We achieve $\approx$13% and $\approx$25% reduction in both the FLOPs and parameter counts of MobileNet-V2 with a 0.27% and more than 1% improvement in top-1 accuracy on the ImageNet-1K and fine-grained image classification datasets (respectively). Code and trained models are available at https://github.com/Nandan91/ULSAM.Comment: Accepted as a conference paper in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV

ROLES OF SCHOOL DISTRICT COMPETITION AND POLITICAL INSTITUTIONS IN PUBLIC SCHOOL SPENDING AND STUDENT ACHIEVEMENT

Equity in school district spending, and equity and productive efficiency in educational outcomes are of paramount importance in the literature on K-12 public education in the US. The research on the effects of school choice (operationalized as inter-school district competition) and local political institutions on unequal school district spending and equity and productive efficiency in educational outcomes is not adequate. This dissertation fills several gaps in the literature by 1) extending the literature on the Public Choice, the Leviathan, the Consolidated Local Government, and the Reformism models that examines the interactive roles of local political institutions and school choice on equity in spending, productive efficiency and equity in student achievement in public schools in metropolitan areas; and 2) modeling the equity effects of school choice and political institutions on school district spending and student achievement. Fixed effects, instrumental variable fixed effects, Hausman-Taylor regression, and Multilevel Linear regression models are utilized on a uniquely compiled longitudinal dataset from several sources, including the Popularly Elected Officials Survey from the US Census Bureau, the Local Education Agency (School District) Longitudinal Finance Survey, the National Education Longitudinal Study (NELS: 1988-92), and the School District Demographics System from the National Center for Education Statistics. Results from fixed effects models lend support for interactive effects of political institutions and inter-school district competition on school district spending. Additive and interactive models do not robustly support the equity effects of inter-school district competition on school district spending. However, results from fixed effects and instrumental variable fixed effects models support the equity effects of political institutions on school district spending in some cases. School districts with more professional political institutions are also more equitable in public education spending. Results show that whereas inter-school district competition has productive efficiency effects on student achievement the political institutions do not. In terms of equity, the inter-school district competition and political institutions have differential effects on student achievement. In regard to the former, results imply that the increased inter-school district competition leads to inequity in students' 10th grade reading scores and 12th grade reading and math scores. In regard to the latter, results suggest that differences in political institutions across school districts lead to inequity in students' 10th and 12th grade reading and math scores. School districts with more professional political institutions also have more equitable student achievement. Student's reading and math scores are generally higher in comparatively higher income quintile school districts than those in comparatively lower income quintile school districts. These findings assume significance as they inform the policymakers in regard to why and how organizational and political contexts matter in bringing desirable educational outcomes. The policymakers can bring organizational and political changes in school districts for achieving the goal of more effective public education