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pro vyhledávání: '"Moitra, Abhishek"'
Publikováno v:
Applied Physics Reviews, 2024
This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highlighting their collective potential for low-power edge computing enviro
Externí odkaz:
http://arxiv.org/abs/2408.12767
Due to the high computation overhead of Vision Transformers (ViTs), In-memory Computing architectures are being researched towards energy-efficient deployment in edge-computing scenarios. Prior works have proposed efficient algorithm-hardware co-desi
Externí odkaz:
http://arxiv.org/abs/2408.12742
The attention module in vision transformers(ViTs) performs intricate spatial correlations, contributing significantly to accuracy and delay. It is thereby important to modulate the number of attentions according to the input feature complexity for op
Externí odkaz:
http://arxiv.org/abs/2404.15185
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024
Transformers have revolutionized various real-world applications from natural language processing to computer vision. However, traditional von-Neumann computing paradigm faces memory and bandwidth limitations in accelerating transformers owing to the
Externí odkaz:
http://arxiv.org/abs/2402.02586
AI chips commonly employ SRAM memory as buffers for their reliability and speed, which contribute to high performance. However, SRAM is expensive and demands significant area and energy consumption. Previous studies have explored replacing SRAM with
Externí odkaz:
http://arxiv.org/abs/2312.03559
Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data representations. While recent SNN algorithmic advances achieve hig
Externí odkaz:
http://arxiv.org/abs/2309.03388
In practical cloud-edge scenarios, where a resource constrained edge performs data acquisition and a cloud system (having sufficient resources) performs inference tasks with a deep neural network (DNN), adversarial robustness is critical for reliabil
Externí odkaz:
http://arxiv.org/abs/2310.06845
Publikováno v:
IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2023
Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers its own pros & cons during inference of Deep Neural Networks (DNNs) on c
Externí odkaz:
http://arxiv.org/abs/2308.00664
Autor:
Bhattacharjee, Abhiroop, Moitra, Abhishek, Kim, Youngeun, Venkatesha, Yeshwanth, Panda, Priyadarshini
Publikováno v:
Great Lakes Symposium on VLSI 2023 (GLSVLSI 2023) conference
In-Memory Computing (IMC) platforms such as analog crossbars are gaining focus as they facilitate the acceleration of low-precision Deep Neural Networks (DNNs) with high area- & compute-efficiencies. However, the intrinsic non-idealities in crossbars
Externí odkaz:
http://arxiv.org/abs/2305.18416
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation. However, their non-linear activation, that is Leaky-Integrate-and-Fire (LIF) neuron, requires addi
Externí odkaz:
http://arxiv.org/abs/2305.18360