NPUs are integrated units that excel in real-time AI tasks on edge devices like smartphones and IoT systems with low power consumption. TPUs are standalone processors designed for large-scale AI workloads in data centers, delivering exceptional performance in deep learning tasks.
Infineon’s Power System Reliability Modeling enhances power reliability in data centers by enabling real-time power supply monitoring, predictive maintenance, and lifetime estimation from component to system level.
This article is a detailed analysis of In-Memory Compute technology, covering its architecture, use cases, recent advancements, and practical implementation strategies to enhance computational efficiency.
EPFL researchers have developed 4M, a next-generation, open-sourced framework for training versatile and scalable multimodal foundation models that go beyond language.
EPFL research investigating the potential impact on education of AI assistants has found that systems like GPT-4 can answer up to 85% of university assessment questions correctly.
Optical sensors are the “eyes” of industrial systems, relying on robust peripherals for reliable image processing. As sensors grow more powerful, challenges like data management, mechanical stress, and thermal issues in compact designs demand innovative solutions.
Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server — a process that’s expensive, energy-intensive and often slow.
Some 80% of weather radiosondes – remote measurement instruments containing plastic, batteries and electronic parts – end up lost in nature after one flight. An EPFL student is set to change that with a new, ultra-lightweight “glidersonde” that can automatically return to where it was launched.
By utilizing the Arduino Portenta Machine Control, Techgest helped manufacturing clients improve real-time monitoring, energy efficiency, and production optimization – all without vendor lock-in or costly retrofits.
Project CETI and Harvard have established a new reinforcement learning framework for rendezvous with whales using autonomous robots, combining sensing from diverse sensor streams