Zobrazeno 1 - 10
of 279
pro vyhledávání: '"PANDA, PRIYADARSHINI"'
Autor:
Li, Yuhang, Panda, Priyadarshini
Large language models (LLMs) have revolutionized natural language processing, albeit at the cost of immense memory and computation requirements. Post-training quantization (PTQ) is becoming the de facto method to reduce the memory footprint and impro
Externí odkaz:
http://arxiv.org/abs/2410.19103
Spike-based Transformer presents a compelling and energy-efficient alternative to traditional Artificial Neural Network (ANN)-based Transformers, achieving impressive results through sparse binary computations. However, existing spike-based transform
Externí odkaz:
http://arxiv.org/abs/2409.19764
Spiking Neural Networks (SNNs) have emerged as a compelling, energy-efficient alternative to traditional Artificial Neural Networks (ANNs) for static image tasks such as image classification and segmentation. However, in the more complex video classi
Externí odkaz:
http://arxiv.org/abs/2409.01564
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
Spiking Neural Networks (SNNs) have gained significant research attention in the last decade due to their potential to drive resource-constrained edge devices. Though existing SNN accelerators offer high efficiency in processing sparse spikes with de
Externí odkaz:
http://arxiv.org/abs/2407.14073
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
Prompt-based Continual Learning (PCL) has gained considerable attention as a promising continual learning solution as it achieves state-of-the-art performance while preventing privacy violation and memory overhead issues. Nonetheless, existing PCL ap
Externí odkaz:
http://arxiv.org/abs/2402.16189
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
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency. However, it often hinges on the availability of training data to mitigate quantization errors, a significant chal
Externí odkaz:
http://arxiv.org/abs/2312.05272