Zobrazeno 1 - 10
of 100
pro vyhledávání: '"SAMPSON, JACK"'
As continuous learning based video analytics continue to evolve, the role of efficient edge servers in efficiently managing vast and dynamic datasets is becoming increasingly crucial. Unlike their compute architecture, storage and archival system for
Externí odkaz:
http://arxiv.org/abs/2410.05435
Autor:
Mishra, Cyan Subhra, Sampson, Jack, Kandmeir, Mahmut Taylan, Narayanan, Vijaykrishnan, Das, Chita R
There is an increasing demand for intelligent processing on ultra-low-power internet of things (IoT) device. Recent works have shown substantial efficiency boosts by executing inferences directly on the IoT device (node) rather than transmitting data
Externí odkaz:
http://arxiv.org/abs/2408.14379
The deployment of Deep Neural Networks in energy-constrained environments, such as Energy Harvesting Wireless Sensor Networks, presents unique challenges, primarily due to the intermittent nature of power availability. To address these challenges, th
Externí odkaz:
http://arxiv.org/abs/2408.13696
In Spiking Neural Networks (SNNs), learning rules are based on neuron spiking behavior, that is, if and when spikes are generated due to a neuron's membrane potential exceeding that neuron's firing threshold, and this spike timing encodes vital infor
Externí odkaz:
http://arxiv.org/abs/2407.19566
Neuromorphic computing has recently gained significant attention as a promising combined approach for developing energy-efficient, parallel computing systems inspired by the human brain. Efficient training algorithms are imperative for the effective
Externí odkaz:
http://arxiv.org/abs/2406.10066
Autor:
Shin, Philip Wootaek, Ahn, Jihyun Janice, Yin, Wenpeng, Sampson, Jack, Narayanan, Vijaykrishnan
It has been shown that many generative models inherit and amplify societal biases. To date, there is no uniform/systematic agreed standard to control/adjust for these biases. This study examines the presence and manipulation of societal biases in lea
Externí odkaz:
http://arxiv.org/abs/2406.05602
Heap memory errors remain a major source of software vulnerabilities. Existing memory safety defenses aim at protecting all objects, resulting in high performance cost and incomplete protection. Instead, we propose an approach that accurately identif
Externí odkaz:
http://arxiv.org/abs/2310.06397
There is an increasing demand for intelligent processing on emerging ultra-low-power internet of things (IoT) devices, and recent works have shown substantial efficiency boosts by executing inference tasks directly on the IoT device (node) rather tha
Externí odkaz:
http://arxiv.org/abs/2204.13106
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