Search Results - "Venkataramani, Swagath"

Accelerating DNN Training Through Selective Localized Learning by Krithivasan, Sarada, Sen, Sanchari, Venkataramani, Swagath, Raghunathan, Anand

“…Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. We…”
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Substitute-and-simplify: A unified design paradigm for approximate and quality configurable circuits by Venkataramani, Swagath, Roy, Kaushik, Raghunathan, Anand

“…Many applications are inherently resilient to inexactness or approximations in their underlying computations. Approximate circuit design is an emerging…”
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MixTrain: accelerating DNN training via input mixing by Krithivasan, Sarada, Sen, Sanchari, Venkataramani, Swagath, Raghunathan, Anand

“…Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. An…”
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Exploiting approximate computing for deep learning acceleration by Chen, Chia-Yu, Choi, Jungwook, Gopalakrishnan, Kailash, Srinivasan, Viji, Venkataramani, Swagath

“…Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of techniques to address challenging artificial intelligence (AI) problems…”
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Quality programmable vector processors for approximate computing by Venkataramani, Swagath, Chippa, Vinay K., Chakradhar, Srimat T., Roy, Kaushik, Raghunathan, Anand

“…Approximate computing leverages the intrinsic resilience of applications to inexactness in their computations, to achieve a desirable trade-off between…”
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DNNDaSher: A Compiler Framework for Dataflow Compatible End-to-End Acceleration on IBM AIU by Sen, Sanchari, Jain, Shubham, Krithivasan, Sarada, Venkataramani, Swagath, Srinivasan, Vijayalakshmi

“…Artificial Intelligence Unit (AIU) is a specialized accelerator card from IBM offering state-of-the-art compute capabilities (100s of TOPS) through…”
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SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks by Sen, Sanchari, Jain, Shubham, Venkataramani, Swagath, Raghunathan, Anand

“…Deep Neural Networks (DNNs) have emerged as the method of choice for solving a wide range of machine learning tasks. The enormous computational demand posed by…”
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AxNN: Energy-efficient neuromorphic systems using approximate computing by Venkataramani, Swagath, Ranjan, Ashish, Roy, Kaushik, Raghunathan, Anand

“…Neuromorphic algorithms, which are comprised of highly complex, large-scale networks of artificial neurons, are increasingly used for a variety of recognition,…”
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Approximate computing for spiking neural networks by Sen, Sanchari, Venkataramani, Swagath, Raghunathan, Anand

“…Spiking Neural Networks (SNNs) are widely regarded as the third generation of artificial neural networks, and are expected to drive new classes of recognition,…”
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Logic Synthesis of Approximate Circuits by Venkataramani, Swagath, Kozhikkottu, Vivek J., Sabne, Amit, Roy, Kaushik, Raghunathan, Anand

“…The ability of several important application domains to tolerate inexactness or approximations in a large fraction of their computations has lead to the advent…”
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Approximate computing and the quest for computing efficiency by Venkataramani, Swagath, Chakradhar, Srimat T., Roy, Kaushik, Raghunathan, Anand

“…Diminishing benefits from technology scaling have pushed designers to look for new sources of computing efficiency. Multicores and heterogeneous…”
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Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations by Raha, Arnab, Venkataramani, Swagath, Raghunathan, Vijay, Raghunathan, Anand

“…Approximate computing is an emerging design paradigm that exploits the intrinsic ability of applications to produce acceptable outputs even when their…”
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SCALEDEEP: A scalable compute architecture for learning and evaluating deep networks by Venkataramani, Swagath, Ranjan, Ashish, Banerjee, Subarno, Das, Dipankar, Avancha, Sasikanth, Jagannathan, Ashok, Durg, Ajaya, Nagaraj, Dheemanth, Kaul, Bharat, Dubey, Pradeep, Raghunathan, Anand

“…Deep Neural Networks (DNNs) have demonstrated state-of-the-art performance on a broad range of tasks involving natural language, speech, image, and video…”
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Efficacy of Pruning in Ultra-Low Precision DNNs by Sen, Sanchari, Venkataramani, Swagath, Raghunathan, Anand

“…Quantization, or reducing the precision of variables and operations, and pruning, or removing neurons and connections are two popular approaches for improving…”
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Dynamic Spike Bundling for Energy-Efficient Spiking Neural Networks by Krithivasan, Sarada, Sen, Sanchari, Venkataramani, Swagath, Raghunathan, Anand

“…Spiking Neural Networks (SNNs), which represent information as sequences of spikes, are gaining interest due to the emergence of low-power hardware platforms…”
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SparCE : Spar sity Aware General-Purpose C ore E xtensions to Accelerate Deep Neural Networks by Sen, Sanchari, Jain, Shubham, Venkataramani, Swagath, Raghunathan, Anand

“…Deep Neural Networks (DNNs) have emerged as the method of choice for solving a wide range of machine learning tasks. The enormous computational demand posed by…”
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SALSA: systematic logic synthesis of approximate circuits by Venkataramani, Swagath, Sabne, Amit, Kozhikkottu, Vivek, Roy, Kaushik, Raghunathan, Anand

“…Approximate computing has emerged as a new design paradigm that exploits the inherent error resilience of a wide range of application domains by allowing…”
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Exploring Spin-Transfer-Torque Devices for Logic Applications by Pajouhi, Zoha, Venkataramani, Swagath, Yogendra, Karthik, Raghunathan, Anand, Roy, Kaushik

“…As CMOS nears the end of the projected scaling roadmap, significant effort has been devoted to the search for new materials and devices that can realize memory…”
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Energy-Efficient Object Detection Using Semantic Decomposition by Panda, Priyadarshini, Venkataramani, Swagath, Sengupta, Abhronil, Raghunathan, Anand, Roy, Kaushik

“…In this brief, we present a new approach to optimize energy efficiency of object detection tasks using semantic decomposition to build a hierarchical…”
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Efficient AI System Design With Cross-Layer Approximate Computing by Venkataramani, Swagath, Sun, Xiao, Wang, Naigang, Chen, Chia-Yu, Choi, Jungwook, Kang, Mingu, Agarwal, Ankur, Oh, Jinwook, Jain, Shubham, Babinsky, Tina, Cao, Nianzheng, Fox, Thomas, Fleischer, Bruce, Gristede, George, Guillorn, Michael, Haynie, Howard, Inoue, Hiroshi, Ishizaki, Kazuaki, Klaiber, Michael, Lo, Shih-Hsien, Maier, Gary, Mueller, Silvia, Scheuermann, Michael, Ogawa, Eri, Schaal, Marcel, Serrano, Mauricio, Silberman, Joel, Vezyrtzis, Christos, Wang, Wei, Yee, Fanchieh, Zhang, Jintao, Ziegler, Matthew, Zhou, Ching, Ohara, Moriyoshi, Lu, Pong-Fei, Curran, Brian, Shukla, Sunil, Srinivasan, Vijayalakshmi, Chang, Leland, Gopalakrishnan, Kailash

“…Advances in deep neural networks (DNNs) and the availability of massive real-world data have enabled superhuman levels of accuracy on many AI tasks and ushered…”
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