TechCrunch Early Stage 2024 Women’s Breakfast: Exploring AI’s impact on founders

In the world of tech, innovation knows no bounds. And at the forefront of this ever-evolving landscape, AI stands tall, casting its transformative spell on everything it touches. But amid the buzz, one crucial question emerges: How is AI shaping the journey of founders? TechCrunch’s Early Stage conference is set to delve deep into this … Read more

Microsoft is working on an Xbox AI chatbot

A mockup of what Microsoft’s Xbox chatbot looks like. | Cath Virginia / The Verge Microsoft is currently testing a new AI-powered Xbox chatbot that can be used to automate support tasks. Sources familiar with Microsoft’s plans tell The Verge that the software giant has been testing an “embodied AI character” that animates when responding … Read more

Outdoor Tech That Works: Stay Warm and Dry in the Harshest Conditions

Following its breakout year in 2023, generative AI (genAI) is finding increasing applications across various industries, including fashion. However, while this is a significant technology, it’s only one aspect of the changes happening in the field. Some innovations may not appear on the catwalks or make headlines but could be essential during mountain expeditions or … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Experimental Results

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Opportunities

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Motivation

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Experiments

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Conclusion & References

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Hardware Details

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices: Arithmetic Intensity

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more

Modeling Workload Interference

:::info This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Minghao Yan, University of Wisconsin-Madison; (2) Hongyi Wang, Carnegie Mellon University; (3) Shivaram Venkataraman, myan@cs.wisc.edu. ::: Table of Links Abstract & Introduction Motivation Opportunities Architecture Overview Proble Formulation: Two-Phase Tuning Modeling Workload Interference Experiments Conclusion & References A. Hardware Details … Read more