What is a dataflow architecture? Why Efficient Computer’s Electron E1 processor design is so radical. How the Electron E1 is able to be so power-efficient. My master’s degree work many decades ago was ...

Abstract: Neural networks for speech separation generally exhibit high computational costs and large memory footprints. Moreover, typical separation networks have a fixed computational graph that ...

Abstract: We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational ...

Covid-19 broke the charts. Decades from now, the pandemic will be visible in the historical data of nearly anything measurable today: an unmistakable spike, dip or jolt that officially began for ...

OpenAI’s recently unveiled o3 model is purportedly its most powerful AI yet, but with one big drawback: it costs ungodly sums of money to run, TechCrunch reports. Announced just over a week ago, o3 ...

Deploying machine learning models on edge devices poses significant challenges due to limited computational resources. When the size and complexity of models increase, even achieving efficient ...

Large language models (LLMs) such as GPT-4o and other modern state-of-the-art generative models like Anthropic’s Claude, Google's PaLM and Meta's Llama have been dominating the AI field recently.

No matter how elegant and clever the design is for a compute engine, the difficulty and cost of moving existing – and sometimes very old – code from the device it currently runs on to that new compute ...