tag: edge AI

Small Vs. Large Language Models
By Ed Sperling - 03 Nov, 2025 - Comments: 0

The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option. The initial goal for small language models (SLMs) — roughly 10 billion parameters or less, compared to more than a trillion parameters in the biggest LLMs — was to leverage them exclusively for inferencing. In... » read more

Why In-Memory Computation Is So Important For Edge AI
By Katherine Derbyshire - 23 Oct, 2025 - Comments: 1

In popular media, “AI” usually means large language models running in expensive, power-hungry data centers. For many applications, though, smaller models running on local hardware are a much better fit. Autonomous vehicles need to respond in real-time, without data transmission delays. Medical and industrial applications often depend on sensitive data that cannot be shared with third par... » read more

A Hybrid Subsystem Architecture To Elevate Edge AI
By Sriram Kalluri - 09 Oct, 2025 - Comments: 0

The world of artificial intelligence is moving beyond the cloud and into our everyday devices from smart sensors to robotics and AR/VR headsets. One of the key components that enables this shift is a neural processing unit (NPU), also known as an AI accelerator, which is a specialized hardware designed to execute AI models. Optimized for neural network, deep learning, and machine learning tasks... » read more

The Rise Of Scalable AI SoCs For The IoT Device Edge
By Nebu Philips - 09 Oct, 2025 - Comments: 0

The landscape of computing is undergoing a profound transformation, with Artificial Intelligence (AI) at its forefront. This shift is particularly evident at the device edge, where traditional System-on-Chip (SoCs) implementations are being reimagined to effectively support demanding AI and machine learning (ML) workloads. This evolution necessitates the development of a new class of AI-capa... » read more

How Grinn And Synaptics Are Accelerating Edge AI Adoption And Innovation
By Synaptics - 09 Oct, 2025 - Comments: 0

From smart cities to industrial automation, organizations are rethinking how and where data is processed. The answer, increasingly, is at the Edge—where devices can analyze information in real time without sending it to the cloud. This approach improves responsiveness, enhances security, and reduces reliance on network connectivity. Recognizing this shift, Grinn Global and Synaptics have p... » read more

Research Bits: Sept. 30
By Jesse Allen - 30 Sep, 2025 - Comments: 0

Hybrid memory for edge training and inference Researchers from CEA-Leti, Université Grenoble Alpes, CEA-List, the French National Centre for Scientific Research (CNRS), the University of Bordeaux, Bordeaux INP, IMS France, Université Paris-Saclay, and the Center for Nanosciences and Nanotechnologies developed a hybrid memory system that combines the traits of ferroelectric capacitors (FeCAP)... » read more

Complex Mix Of Processors At The Edge
By Liz Allan - 18 Aug, 2025 - Comments: 1

With AI changing so fast, it’s a juggle for companies to ensure they can deliver the best performance now while also future-proofing for unknown AI models or a completely different approach to training and inference that may emerge. There are a slew of options for high-end and budget phones, hyperscalers, and low-cost, low-power edge devices, and while GPUs keep making headlines, many designe... » read more

Edge AI: Enabling Smart IoT Applications
By Arm - 13 Aug, 2025 - Comments: 0

As industries race to unlock the next wave of innovation, edge AI is emerging as a game-changer—bringing intelligent, real-time data processing directly to the device level across industries. ‘Edge AI: Enabling Smart IoT Applications’ is your in-depth guide to the transformative potential of artificial intelligence at the edge. This insightful eBook explores 15 powerful use cases that ... » read more

LLMs On The Edge
By Ed Sperling - 16 Jun, 2025 - Comments: 0

Nearly all the data input for AI so far has been text, but that's about to change. In the future, that input likely will include video, voice, as well as other types of data, causing a massive increase in the amount of data that needs to be modeled and the compute resources necessary to make it all work. This is hard enough in hyperscale data centers, which are sprouting up everywhere to handle... » read more

Mobile Chip Challenges In The AI Era
By Liz Allan - 05 Jun, 2025 - Comments: 0

Leading smart phone vendors are struggling to keep pace with the rising compute and power demands of localized generative AI, standard phone functions, and the need to move more data back and forth between handsets and the cloud. In addition to edge functions, such as facial recognition and other on-device apps, phones must accommodate a continuous stream of new communications protocols, and... » read more

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