Classical Computing vs. Machine Learning and Edge AI Techniques in Various Application Domains


Machine Learning (ML) algorithms have revolutionized various domains by enabling data-driven decision-making and automation. The deployment of ML models on embedded edge devices, characterized by their constrained computational resources and low power requirements, presents unique challenges and opportunities. As the digital world continues to generate increasingly complex and high-volume da... » read more

Report: The AI Efficiency Boom


Artificial Intelligence (AI) is undergoing a fundamental transformation. While early AI models were large, compute-heavy, and dependent on cloud processing, a new wave of efficiency-driven innovations is moving AI inference—the generation of model results—to the edge. Smaller models, improved memory and compute performance, and the need for privacy, low latency, and energy efficiency are dr... » read more

Embedded GPU: An Open-Source And Configurable RISC-V GPU Platform for TinyAI Devices (EPFL)


A new technical paper titled "e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications" was published by researchers at EPFL. Abstract "Graphics processing units (GPUs) excel at parallel processing, but remain largely unexplored in ultra-low-power edge devices (TinyAI) due to their power and area limitations, as well as the lack of suitable programming... » read more

Getting Real About AI Processors


There’s a lot of confusion and hype around AI. Nearly every service, product or subject area in the technology industry now has an AI label. A lot of this is valid and there’s no doubt that AI is opening up new capabilities and higher productivity across all industries. This white paper categorises AI and related hardware options, with a particular focus on on-device (i.e. edge) AI, givi... » read more

Efficient TNN Inference on RISC-V Processing Cores With Minimal HW Overhead


A new technical paper titled "xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems" was published by researchers at ETH Zurich and Universita di Bologna. Abstract "Ternary neural networks (TNNs) offer a superior accuracy-energy trade-off compared to binary neural networks. However, until now, they have required specialized accelerators to realize their effic... » read more

Memory Devices-Based Bayesian Neural Networks For Edge AI


A new technical paper titled "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks" was published by researchers at Université Grenoble Alpes, CEA, LETI, and CNRS. Abstract: "Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering... » read more