Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Pothukuchi P"'
Publikováno v:
The 15th Annual Non-Volatile Memories Workshop (NVMW), March, 2024
Neural interfaces read the activity of biological neurons to help advance the neurosciences and offer treatment options for severe neurological diseases. The total number of neurons that are now being recorded using multi-electrode interfaces is doub
Externí odkaz:
http://arxiv.org/abs/2409.17541
Publikováno v:
The 1st Workshop on Hot Topics in Ethical Computer Systems, April, 2024
Designs for implanted brain-computer interfaces (BCIs) have increased significantly in recent years. Each device promises better clinical outcomes and quality-of-life improvements, yet due to severe and inflexible safety constraints, progress require
Externí odkaz:
http://arxiv.org/abs/2409.17496
Publikováno v:
The 1st Workshop on Hot Topics in Ethical Computer Systems, April, 2024
Brain-computer interfaces (BCIs) connect biological neurons in the brain with external systems like prosthetics and computers. They are increasingly incorporating processing capabilities to analyze and stimulate neural activity, and consequently, pos
Externí odkaz:
http://arxiv.org/abs/2409.17445
Autor:
Pothukuchi, Raghavendra Pradyumna, Lufkin, Leon, Shen, Yu Jun, Simon, Alejandro, Thorstenson, Rome, Trevisan, Bernardo Eilert, Tu, Michael, Yang, Mudi, Foxman, Ben, Pothukuchi, Viswanatha Srinivas, Epping, Gunnar, Kyaw, Thi Ha, Jongkees, Bryant J, Ding, Yongshan, Busemeyer, Jerome R, Cohen, Jonathan D, Bhattacharjee, Abhishek
Research progress in quantum computing has, thus far, focused on a narrow set of application domains. Expanding the suite of quantum application domains is vital for the discovery of new software toolchains and architectural abstractions. In this wor
Externí odkaz:
http://arxiv.org/abs/2309.00597
Continual learning on sequential data is critical for many machine learning (ML) deployments. Unfortunately, LSTM networks, which are commonly used to learn on sequential data, suffer from catastrophic forgetting and are limited in their ability to l
Externí odkaz:
http://arxiv.org/abs/2305.17244
Side-channel attacks that use machine learning (ML) for signal analysis have become prominent threats to computer security, as ML models easily find patterns in signals. To address this problem, this paper explores using Adversarial Machine Learning
Externí odkaz:
http://arxiv.org/abs/2302.01474
Autor:
Sriram, Karthik, Pothukuchi, Raghavendra Pradyumna, Gerasimiuk, Michał, Ye, Oliver, Ugur, Muhammed, Manohar, Rajit, Khandelwal, Anurag, Bhattacharjee, Abhishek
Hull is an accelerator-rich distributed implantable Brain-Computer Interface (BCI) that reads biological neurons at data rates that are 2-3 orders of magnitude higher than the prior state of art, while supporting many neuroscientific applications. Pr
Externí odkaz:
http://arxiv.org/abs/2301.03103
Autor:
Vesely, Jan, Pothukuchi, Raghavendra Pradyumna, Joshi, Ketaki, Gupta, Samyak, Cohen, Jonathan D., Bhattacharjee, Abhishek
This paper discusses our proposal and implementation of Distill, a domain-specific compilation tool based on LLVM to accelerate cognitive models. Cognitive models explain the process of cognitive function and offer a path to human-like artificial int
Externí odkaz:
http://arxiv.org/abs/2110.15425
Recently, Unmanned Aerial Vehicle (UAV) based communications systems have attracted increasing research and commercial interest due to their cost effective deployment and ease of mobility.During natural disasters and emergencies, such networks are ex
Externí odkaz:
http://arxiv.org/abs/2105.10755
Autor:
Pothukuchi, Raghavendra Pradyumna, Pothukuchi, Sweta Yamini, Voulgaris, Petros, Schwing, Alexander, Torrellas, Josep
Publikováno v:
2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 2021, pp. 888-901
The security of computers is at risk because of information leaking through physical outputs such as power, temperature, or electromagnetic (EM) emissions. Attackers can use advanced signal measurement and analysis to recover sensitive data from thes
Externí odkaz:
http://arxiv.org/abs/1907.09440