Zobrazeno 1 - 10
of 227
pro vyhledávání: '"Sengupta, Abhronil"'
Autor:
Bal, Malyaban, Sengupta, Abhronil
Spiking neural networks (SNNs) are posited as a biologically plausible alternative to conventional neural architectures, with their core computational framework resting on the extensively studied leaky integrate-and-fire (LIF) neuron design. The stat
Externí odkaz:
http://arxiv.org/abs/2406.02923
Equilibrium Propagation (EP) is a biologically plausible local learning algorithm initially developed for convergent recurrent neural networks (RNNs), where weight updates rely solely on the connecting neuron states across two phases. The gradient ca
Externí odkaz:
http://arxiv.org/abs/2405.02546
Despite the growing prevalence of large language model (LLM) architectures, a crucial concern persists regarding their energy and power consumption, which still lags far behind the remarkable energy efficiency of the human brain. Recent strides in sp
Externí odkaz:
http://arxiv.org/abs/2405.02543
Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on neuromorphic hardware. However, the majority of existing studies on SNNs
Externí odkaz:
http://arxiv.org/abs/2404.17719
Spiking Neural Networks (SNNs), providing more realistic neuronal dynamics, have shown to achieve performance comparable to Artificial Neural Networks (ANNs) in several machine learning tasks. Information is processed as spikes within SNNs in an even
Externí odkaz:
http://arxiv.org/abs/2402.01782
Autor:
Han, Zhuangyu, Sengupta, Abhronil
Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-power event-driven computing. Application of neuromorphic computing in rob
Externí odkaz:
http://arxiv.org/abs/2312.15805
Reliability issues stemming from device level non-idealities of non-volatile emerging technologies like ferroelectric field-effect transistors (FeFET), especially at scaled dimensions, cause substantial degradation in the accuracy of In-Memory crossb
Externí odkaz:
http://arxiv.org/abs/2312.15444
Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50% of human brain cells - in brain-inspired neuromorphic computing remain in infancy. This paper seeks to delve deeper into various key aspects o
Externí odkaz:
http://arxiv.org/abs/2312.10925
Autor:
Yuan, Yifan, Kotiuga, Michele, Park, Tae Joon, Ni, Yuanyuan, Saha, Arnob, Zhou, Hua, Sadowski, Jerzy T., Al-Mahboob, Abdullah, Yu, Haoming, Du, Kai, Zhu, Minning, Deng, Sunbin, Bisht, Ravindra S., Lyu, Xiao, Wu, Chung-Tse Michael, Ye, Peide D., Sengupta, Abhronil, Cheong, Sang-Wook, Xu, Xiaoshan, Rabe, Karin M., Ramanathan, Shriram
Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO3 in
Externí odkaz:
http://arxiv.org/abs/2311.12200
Autor:
Bal, Malyaban, Sengupta, Abhronil
Large language Models (LLMs), though growing exceedingly powerful, comprises of orders of magnitude less neurons and synapses than the human brain. However, it requires significantly more power/energy to operate. In this work, we propose a novel bio-
Externí odkaz:
http://arxiv.org/abs/2308.10873