Zobrazeno 1 - 10
of 14 346
pro vyhledávání: '"Priyadarshini, A."'
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
Deep neural networks trained on biased data often inadvertently learn unintended inference rules, particularly when labels are strongly correlated with biased features. Existing bias mitigation methods typically involve either a) predefining bias typ
Externí odkaz:
http://arxiv.org/abs/2410.15094
Spiking Neural Networks (SNNs) present a compelling and energy-efficient alternative to traditional Artificial Neural Networks (ANNs) due to their sparse binary activation. Leveraging the success of the transformer architecture, the spiking transform
Externí odkaz:
http://arxiv.org/abs/2409.19764
We propose a minimal power white box adversarial attack for Deep Learning based Automatic Modulation Classification (AMC). The proposed attack uses the Golden Ratio Search (GRS) method to find powerful attacks with minimal power. We evaluate the effi
Externí odkaz:
http://arxiv.org/abs/2409.11454
Autor:
Bagchi, Arjun, Chakraborty, Pronoy, Chakrabortty, Shankhadeep, Fredenhagen, Stefan, Grumiller, Daniel, Pandit, Priyadarshini
We consider Carrollian conformal field theories in two dimensions and construct the boundary Carrollian conformal algebra (BCCA), opening up innumerable possibilities for further studies, given the growing relevance of Carrollian symmetries. We prove
Externí odkaz:
http://arxiv.org/abs/2409.01094
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
Autor:
Martinez, Alejandra, Tovar, Laura, Amparan, Carla Irigoyen, Gonzalez, Karen, Edayath, Prajina, Pennathur, Priyadarshini, Pennathur, Arunkumar
Occupational exoskeletons promise to alleviate musculoskeletal injuries among industrial workers. Knowledge of the usability of the exoskeleton designs with respect to the user device interaction points, and the problems in design features, functions
Externí odkaz:
http://arxiv.org/abs/2408.02852
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