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Sasikanth Avancha

Bio: Sasikanth Avancha is an academic researcher from Intel. The author has contributed to research in topics: Bluetooth & Deep learning. The author has an hindex of 23, co-authored 67 publications receiving 5115 citations. Previous affiliations of Sasikanth Avancha include Fujitsu & Massachusetts Institute of Technology.


Papers
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Journal Article
TL;DR: This chapter identifies the vulnerabilities associated with the operational paradigms currently employed by Wireless Sensor Networks and a framework for implementing security in WSNs, which identifies the security measures necessary to mitigate the identified vulnerabilities.
Abstract: This chapter identifies the vulnerabilities associated with the operational paradigms currently employed by Wireless Sensor Networks. A survey of current WSN security research is presented. The security issues of Mobile Ad-Hoc Networks and infrastructure supported wireless networks are briefly compared and contrasted to the security concerns of Wireless Sensor Networks. A framework for implementing security in WSNs, which identifies the security measures necessary to mitigate the identified vulnerabilities is defined.

2,939 citations

Patent
31 Aug 2004
TL;DR: In this article, a system and method uses wireless communication devices as beacons to determine the relative location of another target wireless communication device, where the beacons transmit identifying information that the target device can use to determine identity of the beacon.
Abstract: A system and method uses wireless communication devices as beacons to determine the relative location of another target wireless communication device. The beacons transmit identifying information that the target device can use to determine the identity of the beacon. The target device can measure the received intensity of the beacon transmissions and determine an associated beacon that best satisfies a specified criteria (e.g., largest signal strength) using a procedure of the present invention that discriminates between multiple beacons.

282 citations

Journal ArticleDOI
TL;DR: This survey examines the privacy requirements of mobile computing technologies that have the potential to transform healthcare and develops a conceptual privacy framework for mHealth, itemize the privacy properties needed in mHealth systems, and discusses the technologies that could support privacy-sensitive m health systems.
Abstract: Information technology can improve the quality, efficiency, and cost of healthcare. In this survey, we examine the privacy requirements of mobile computing technologies that have the potential to transform healthcare. Such mHealth technology enables physicians to remotely monitor patients' health and enables individuals to manage their own health more easily. Despite these advantages, privacy is essential for any personal monitoring technology. Through an extensive survey of the literature, we develop a conceptual privacy framework for mHealth, itemize the privacy properties needed in mHealth systems, and discuss the technologies that could support privacy-sensitive mHealth systems. We end with a list of open research questions.

228 citations

Proceedings ArticleDOI
24 Jun 2017
TL;DR: SCALEDEEP is a dense, scalable server architecture, whose processing, memory and interconnect subsystems are specialized to leverage the compute and communication characteristics of DNNs, and primarily targets DNN training, as opposed to only inference or evaluation.
Abstract: Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad range of tasks involving natural language, speech, image, and video processing, and are deployed in many real world applications. However, DNNs impose significant computational challenges owing to the complexity of the networks and the amount of data they process, both of which are projected to grow in the future. To improve the efficiency of DNNs, we propose ScaleDeep, a dense, scalable server architecture, whose processing, memory and interconnect subsystems are specialized to leverage the compute and communication characteristics of DNNs. While several DNN accelerator designs have been proposed in recent years, the key difference is that ScaleDeep primarily targets DNN training, as opposed to only inference or evaluation. The key architectural features from which ScaleDeep derives its efficiency are: (i) heterogeneous processing tiles and chips to match the wide diversity in computational characteristics (FLOPs and Bytes/FLOP ratio) that manifest at different levels of granularity in DNNs, (ii) a memory hierarchy and 3-tiered interconnect topology that is suited to the memory access and communication patterns in DNNs, (iii) a low-overhead synchronization mechanism based on hardware data-flow trackers, and (iv) methods to map DNNs to the proposed architecture that minimize data movement and improve core utilization through nested pipelining. We have developed a compiler to allow any DNN topology to be programmed onto ScaleDeep, and a detailed architectural simulator to estimate performance and energy. The simulator incorporates timing and power models of ScaleDeep's components based on synthesis to Intel's 14nm technology. We evaluate an embodiment of ScaleDeep with 7032 processing tiles that operates at 600 MHz and has a peak performance of 680 TFLOPs (single precision) and 1.35 PFLOPs (half-precision) at 1.4KW. Across 11 state-of-the-art DNNs containing 0.65M-14.9M neurons and 6.8M-145.9M weights, including winners from 5 years of the ImageNet competition, ScaleDeep demonstrates 6x-28x speedup at iso-power over the state-of-the-art performance on GPUs.

214 citations

Posted Content
TL;DR: The results show that deep learning training using BFLOAT16 tensors achieves the same state-of-the-art (SOTA) results across domains as FP32 tensors in the same number of iterations and with no changes to hyper-parameters.
Abstract: This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training across image classification, speech recognition, language modeling, generative networks and industrial recommendation systems. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can represent is the same as that of IEEE 754 floating-point format (FP32) and conversion to/from FP32 is simple. Maintaining the same range as FP32 is important to ensure that no hyper-parameter tuning is required for convergence; e.g., IEEE 754 compliant half-precision floating point (FP16) requires hyper-parameter tuning. In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16. We have implemented a method to emulate BFLOAT16 operations in Tensorflow, Caffe2, IntelCaffe, and Neon for our experiments. Our results show that deep learning training using BFLOAT16 tensors achieves the same state-of-the-art (SOTA) results across domains as FP32 tensors in the same number of iterations and with no changes to hyper-parameters.

166 citations


Cited by
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Posted Content
TL;DR: This work proposes a small DNN architecture called SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510x smaller than AlexNet).
Abstract: Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). The SqueezeNet architecture is available for download here: this https URL

5,904 citations

Book ChapterDOI
08 Sep 2018
TL;DR: ShuffleNet V2 as discussed by the authors proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs, based on a series of controlled experiments, and derives several practical guidelines for efficient network design.
Abstract: Currently, the neural network architecture design is mostly guided by the indirect metric of computation complexity, i.e., FLOPs. However, the direct metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical guidelines for efficient network design. Accordingly, a new architecture is presented, called ShuffleNet V2. Comprehensive ablation experiments verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff.

3,393 citations

Proceedings ArticleDOI
11 May 2003
TL;DR: The random-pairwise keys scheme is presented, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication and quorum-based revocation.
Abstract: Key establishment in sensor networks is a challenging problem because asymmetric key cryptosystems are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. We present three new mechanisms for key establishment using the framework of pre-distributing a random set of keys to each node. First, in the q-composite keys scheme, we trade off the unlikeliness of a large-scale network attack in order to significantly strengthen random key predistribution's strength against smaller-scale attacks. Second, in the multipath-reinforcement scheme, we show how to strengthen the security between any two nodes by leveraging the security of other links. Finally, we present the random-pairwise keys scheme, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication and quorum-based revocation.

3,125 citations

01 Apr 1997
TL;DR: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity.
Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

2,188 citations