MIPI In Next Generation Of AI IoT Devices At The Edge


The history of data processing begins in the 1960s with centralized on-site mainframes that later evolved into distributed client servers. In the beginning of this century, centralized cloud computing became attractive and began to gain momentum, becoming one of the most popular computing tools today. In recent years however, we have seen an increase in the demand for processing at the edge or ... » read more

The Implications Of AI Everywhere: From Data Center To Edge


Generative AI has upped the ante on the transformative force of AI, driving profound implications across all aspects of our everyday lives. Over the past year, we have seen AI capabilities placed firmly in the hands of consumers. The recent news and product announcements emerging from MWC 2024 highlighted what we can expect to see from the next wave of generative AI applications. AI will be eve... » read more

Broad Impact From Accelerating Tech Cycles


Experts at the Table: Semiconductor Engineering sat down to discuss the impact of leading edge technologies such as generative AI in data centers, AR/VR, and security architectures for connected devices, with Michael Kurniawan, business strategy manager at Accenture; Kaushal Vora, senior director and head of business acceleration and ecosystem at Renesas Electronics; Paul Karazuba, vice preside... » read more

Generative AI On Mobile Is Running On The Arm CPU


By Adnan Al-Sinan and Gian Marco Iodice 2023 was the year that showcased an impressive number of use cases powered by generative AI. This disruptive form of artificial intelligence (AI) technology is at the heart OpenAI's ChatGPT and Google’s Gemini AI model, with it demonstrating the opportunity to simplify work and advance education through generating text, images, or even audio content ... » read more

ML-Assisted IC Test Binning With Real-Time Prediction At The Edge


IC Test is a critical part of semiconductor manufacturing and proper die binning and material disposition has an important impact on the overall yield and on the process monitoring and failure mode diagnostics. Edge analytics are becoming an increasingly important aspect of die disposition. By intercepting parts in real-time at the wafer test step, we can save downstream processing needs. In th... » read more

Requirements For The Efficient Implementation Of AI Solutions On Edge Devices


By André Schneider, Olaf Enge-Rosenblatt, and Björn Zeugmann In recent years, there has been a growing tendency to implement data-driven approaches for the continuous monitoring of industrial plants as part of digitalization and Industry 4.0 initiatives. The hope is to detect critical conditions at an early stage, minimize maintenance and downtimes, and continuously achieve high product qu... » read more

AI Races To The Edge


AI is becoming increasingly sophisticated and pervasive at the edge, pushing into new application areas and even taking on some of the algorithm training that has been done almost exclusively in large data centers using massive sets of data. There are several key changes behind this shift. The first involves new chip architectures that are focused on processing, moving, and storing data more... » read more

Maximizing Edge Intelligence Requires More Than Computing


By Toshi Nishida, Avik W. Ghosh, Swaminathan Rajaraman, and Mircea Stan Commercial-off-the-shelf (COTS) components have enabled a commodity market for Wi-Fi-connected appliances, consumer products, infrastructure, manufacturing, vehicles, and wearables. However, the vast majority of connected systems today are deployed at the edge of the network, near the end user or end application, opening... » read more

Unlocking The Power Of Edge Computing With Large Language Models


In recent years, Large Language Models (LLMs) have revolutionized the field of artificial intelligence, transforming how we interact with devices and the possibilities of what machines can achieve. These models have demonstrated remarkable natural language understanding and generation abilities, making them indispensable for various applications. However, LLMs are incredibly resource-intensi... » read more

Software Stack For Edge AI Performance


Developing an agile software stack is important for successful AI deployment on the edge. We regularly encounter new machine learning models created from multiple AI frameworks that leverage the latest primitives and state-of-the-art ML model topologies. This Cambrian explosion has resulted from a fertile open-source community that has embraced AI and is now fueling a wide proliferation of ML m... » read more

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