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pro vyhledávání: '"Shen, John P."'
Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding compute demands of AI workloads. Recent neuroscience theory on intelli
Externí odkaz:
http://arxiv.org/abs/2405.11844
Autor:
Venkatachalam, Shanmuga, Nair, Harideep, Vellaisamy, Prabhu, Zhou, Yongqi, Youssfi, Ziad, Shen, John Paul
Each person has a unique gait, i.e., walking style, that can be used as a biometric for personal identification. Recent works have demonstrated effective gait recognition using deep neural networks, however most of these works predominantly focus on
Externí odkaz:
http://arxiv.org/abs/2404.15312
Autor:
Nair, Harideep, Vellaisamy, Prabhu, Lin, Tsung-Han, Wang, Perry, Blanton, Shawn, Shen, John Paul
General Matrix Multiply (GEMM) hardware, employing large arrays of multiply-accumulate (MAC) units, perform bulk of the computation in deep learning (DL). Recent trends have established 8-bit integer (INT8) as the most widely used precision for DL in
Externí odkaz:
http://arxiv.org/abs/2402.19376
Autor:
Vellaisamy, Prabhu, Shen, John Paul
Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient applicati
Externí odkaz:
http://arxiv.org/abs/2205.14248
Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitecture framework for implementing TNNs and demonstrated competitive perfo
Externí odkaz:
http://arxiv.org/abs/2205.07410
Publikováno v:
2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2021, pp. 266-271
Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex. In contrast to compute-intensive deep neural networks that employ separate training and
Externí odkaz:
http://arxiv.org/abs/2105.13262
Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc. These applications typically involve small, low-power devices on the edge that collect and process real-t
Externí odkaz:
http://arxiv.org/abs/2102.09200
A set of highly-optimized custom macro extensions is developed for a 7nm CMOS cell library for implementing Temporal Neural Networks (TNNs) that can mimic brain-like sensory processing with extreme energy efficiency. A TNN prototype (13,750 neurons a
Externí odkaz:
http://arxiv.org/abs/2012.05419
Temporal Neural Networks (TNNs) use time as a resource to represent and process information, mimicking the behavior of the mammalian neocortex. This work focuses on implementing TNNs using off-the-shelf digital CMOS technology. A microarchitecture fr
Externí odkaz:
http://arxiv.org/abs/2009.00457
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