What Will That Chip Cost?


In the past, analysts, consultants, and many other experts attempted to estimate the cost of a new chip implemented in the latest process technology. They concluded that by the 3nm node, only a few companies would be able to afford them — and by the time they got into the angstrom range, probably nobody would. Much has changed over the past few process nodes. Increasing numbers of startups... » read more

Optimization Of The Interface Between The PD And The AFE In High-Speed, High-Density Optical Receivers


A technical paper titled “Optimizing the Photodetector/Analog Front-End Interface in Optical Communication Receivers” was published by researchers at University of Toronto. Abstract: "This article addresses the optimization of the interface between the photodetector (PD) and the analog front-end in high-speed, high-density optical communication receivers. Specifically, the article focuses... » read more

Energy Usage in Layers Of Computing (SLAC)


A technical paper titled “Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining” was published by researchers at SLAC National Laboratory and Stanford University. Abstract: "Estimates of energy usage in layers of computing from devices to algorithms have bee... » read more

Big Changes Ahead For Photomask Technology


The move to curvilinear shapes on photomasks is gaining steam after years of promise as a way of improving yield, lowering defectivity, and reducing wasted space on a die — all of which are essential for both continued scaling and improved reliability in semiconductors. Interest in this approach ran high at this year's SPIE Photomask Technology + EUV Lithography Conference. Put simply, cur... » read more

Applying Machine Learning to EDA, FPGA Design Automation Tools


A technical paper titled “Application of Machine Learning in FPGA EDA Tool Development” was published by researchers at the University of Texas Dallas. Abstract: "With the recent advances in hardware technologies like advanced CPUs and GPUs and the large availability of open-source libraries, machine learning has penetrated various domains, including Electronics Design Automation (EDA). E... » read more

Neuromorphic Hardware Accelerator For Heterogeneous Many-Accelerator SoCs


A technical paper titled “SpikeHard: Efficiency-Driven Neuromorphic Hardware for Heterogeneous Systems-on-Chip” was published by researchers at Columbia University. Abstract: "Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed wi... » read more

Network-on-Chips Enabling Artificial Intelligence/Machine Learning Everywhere


Recently, I attended the AI HW Summit in Santa Clara and Autosens in Brussels. Artificial intelligence and machine learning (AI/ML) were critical themes for both events, albeit from different angles. While AI/ML as a buzzword is very popular these days in all its good and bad ways, in discussions with customers and prospects, it became clear that we need to be precise in defining what type of A... » read more

Thoughts On AI Consciousness


By Anda Ioana Enescu Buyruk and Catalin Tudor The rapid advancement of artificial intelligence (AI) has sparked profound discussions regarding the possibility of AI systems achieving consciousness. Such a development carries immense implications, forcing us to redirect our focus from studying the behavior of other organisms to scrutinizing ourselves. This article will delve into the concept ... » read more

When And Where To Implement AI/ML In Fabs


Deciphering complex interactions between variables is where machine learning and deep learning shine, but figuring out exactly how ML-based systems will be most useful is the job of engineers. The challenge is in pairing their domain expertise with available ML tools to maximize the value of both. This depends on sufficient quantities of good data, highly optimized algorithms, and proper tra... » read more

Photonic-Electronic SmartNIC With Fast and Energy-Efficient Photonic Computing Cores (MIT)


A technical paper titled “Lightning: A Reconfigurable Photonic-Electronic SmartNIC for Fast and Energy-Efficient Inference” was published by researchers at Massachusetts Institute of Technology (MIT). Abstract: "The massive growth of machine learning-based applications and the end of Moore's law have created a pressing need to redesign computing platforms. We propose Lightning, the first ... » read more

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