Using Silicon Photonics To Reduce Latency On Edge Devices


A new technical paper titled "Delocalized photonic deep learning on the internet’s edge" was published by researchers at MIT and Nokia Corporation. “Every time you want to run a neural network, you have to run the program, and how fast you can run the program depends on how fast you can pipe the program in from memory. Our pipe is massive — it corresponds to sending a full feature-leng... » read more

Complex Tradeoffs In Inferencing Chips


Designing AI/ML inferencing chips is emerging as a huge challenge due to the variety of applications and the highly specific power and performance needs for each of them. Put simply, one size does not fit all, and not all applications can afford a custom design. For example, in retail store tracking, it's acceptable to have a 5% or 10% margin of error for customers passing by a certain aisle... » read more

Training a ML model On An Intelligent Edge Device Using Less Than 256KB Memory


A new technical paper titled "On-Device Training Under 256KB Memory" was published by researchers at MIT and MIT-IBM Watson AI Lab. “Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a bro... » read more

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

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