Zobrazeno 1 - 10
of 575
pro vyhledávání: '"RAGHUNATHAN, ANAND"'
Supervised deep learning has emerged as an effective tool for carrying out power side-channel attacks on cryptographic implementations. While increasingly-powerful deep learning-based attacks are regularly published, comparatively-little work has gon
Externí odkaz:
http://arxiv.org/abs/2410.22425
Autor:
Thakuria, Niharika, Malhotra, Akul, Thirumala, Sandeep K., Elangovan, Reena, Raghunathan, Anand, Gupta, Sumeet K.
Ternary Deep Neural Networks (DNN) have shown a large potential for highly energy-constrained systems by virtue of their low power operation (due to ultra-low precision) with only a mild degradation in accuracy. To enable an energy-efficient hardware
Externí odkaz:
http://arxiv.org/abs/2408.13617
Event cameras have emerged as a promising sensing modality for autonomous navigation systems, owing to their high temporal resolution, high dynamic range and negligible motion blur. To process the asynchronous temporal event streams from such sensors
Externí odkaz:
http://arxiv.org/abs/2403.15717
In-memory computing (IMC) with non-volatile memories (NVMs) has emerged as a promising approach to address the rapidly growing computational demands of Deep Neural Networks (DNNs). Mapping DNN layers spatially onto NVM-based IMC accelerators achieves
Externí odkaz:
http://arxiv.org/abs/2312.03146
Autor:
Nagarajan, Amrit, Raghunathan, Anand
Transformers have rapidly increased in popularity in recent years, achieving state-of-the-art performance in processing text, images, audio and video. However, Transformers present large computational requirements for both training and inference, and
Externí odkaz:
http://arxiv.org/abs/2312.12385
Progress in artificial intelligence and machine learning over the past decade has been driven by the ability to train larger deep neural networks (DNNs), leading to a compute demand that far exceeds the growth in hardware performance afforded by Moor
Externí odkaz:
http://arxiv.org/abs/2308.02024
Transformers have achieved great success in a wide variety of natural language processing (NLP) tasks due to the attention mechanism, which assigns an importance score for every word relative to other words in a sequence. However, these models are ve
Externí odkaz:
http://arxiv.org/abs/2303.07470
Autor:
Henkel, Jörg, Li, Hai, Raghunathan, Anand, Tahoori, Mehdi B., Venkataramani, Swagath, Yang, Xiaoxuan, Zervakis, Georgios
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past deca
Externí odkaz:
http://arxiv.org/abs/2210.00497
Autor:
Haensch, Wilfried, Raghunathan, Anand, Roy, Kaushik, Chakrabarti, Bhaswar, Phatak, Charudatta M., Wang, Cheng, Guha, Supratik
Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of today. They are limited by the von Neumann bottleneck: the high cost in e
Externí odkaz:
http://arxiv.org/abs/2206.08735
Autor:
Thakuria, Niharika, Elangovan, Reena, Thirumala, Sandeep K, Raghunathan, Anand, Gupta, Sumeet K.
We propose 2D Piezoelectric FET (PeFET) based compute-enabled non-volatile memory for ternary deep neural networks (DNNs). PeFETs consist of a material with ferroelectric and piezoelectric properties coupled with Transition Metal Dichalcogenide chann
Externí odkaz:
http://arxiv.org/abs/2203.16416