Simplifying AI Edge Deployment


Barrie Mullins, vice president of product at Flex Logix, explains how a programmable accelerator chip can simplify semiconductor design at the edge, where chips need to be high performance as well as low power, yet developing everything from scratch is too expensive and time-consuming. Programmability allows these systems to stay current with changes in algorithms, which can affect everything f... » read more

Novel In-Pixel-in-Memory (P2M) Paradigm for Edge Intelligence (USC)


A new technical paper titled "A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications" was published by researchers at University of Southern California (USC). According to the paper, "we propose a novel Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution, batch normaliza... » read more

Efficient Neuromorphic AI Chip: “NeuroRRAM”


New technical paper titled "A compute-in-memory chip based on resistive random-access memory" was published by a team of international researchers at Stanford, UCSD, University of Pittsburgh, University of Notre Dame and Tsinghua University. The paper's abstract states "by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present ... » read more

AI Power Consumption Exploding


Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable. To a large extent, this is because the field is new, exciting, and rapidly growing. It is being designed to break new ground in terms of accuracy or capability. Today, that means bigger models and larger training sets, which require exponential increases in processin... » read more

Reservoir Computing HW Based on a CMOS-Compatible FeFET


A new technical paper titled "Reservoir computing on a silicon platform with a ferroelectric field-effect transistor" was published by researchers at the University of Tokyo. Researchers report "reservoir computing hardware based on a ferroelectric field-effect transistor (FeFET) consisting of silicon and ferroelectric hafnium zirconium oxide. The rich dynamics originating from the ferroelec... » read more

Bespoke Silicon Redefines Custom ASICs


Semiconductor Engineering sat down to discuss bespoke silicon and what's driving that customization with Kam Kittrell, vice president of product management in the Digital & Signoff group at Cadence; Rupert Baines, chief marketing officer at Codasip; Kevin McDermott, vice president of marketing at Imperas; Mo Faisal, CEO of Movellus; Ankur Gupta, vice president and general manager of Siemens... » read more

Week in Review: Design, Low Power


Acquisitions Renesas completed its acquisition of Reality Analytics, which specializes in embedded AI and TinyML solutions for advanced non-visual sensing in automotive, industrial and commercial products. Siemens Digital Industries Software will acquire Zona Technology, which develops aerospace simulation software. Siemens plans to integrate that software into its wXcelerator and Simcenter... » read more

Edge-AI Hardware for Extended Reality


New technical paper titled "Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications" from researchers at Indian Institute of Technology Delhi and Reality Labs Research, Meta. Abstract "Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR w... » read more

Using AI To Speed Up Edge Computing


AI is being designed into a growing number of chips and systems at the edge, where it is being used to speed up the processing of massive amounts of data, and to reduce power by partitioning and prioritization. That, in turn, allows systems to act upon that data more rapidly. Processing data at the edge rather than in the cloud provides a number of well-documented benefits. Because the physi... » read more

MIPI In Next Generation Of AI IoT Devices At The Edge


The history of data processing begins in the 1960’s 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... » read more

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