tag: machine learning

Harness System-Level Data To Optimize Many-Core AI And ML Chips
By Siemens EDA - 10 Aug, 2021 - Comments: 0

The novel multicore architectures of SoCs for machine learning (ML) and artificial intelligence (AI) applications are expected to deliver huge improvements in power efficiency. However, chip development teams and the customers for their devices face the growing complexity of hardware-software co-optimization, validation, and debug. In short, these SoCs are increasingly difficult to validate and... » read more

Reducing Rework In CMP: An Enhanced Machine Learning-Based Hybrid Metrology Approach
By Vamsi Velidandla - 10 Aug, 2021 - Comments: 0

By Vamsi Velidandla, John Hauck, Zhuo Chen, Joshua Frederick, and Zhihui Jiao The semiconductor industry is constantly marching toward thinner films and complex geometries with smaller dimensions, as well as newer materials. The number of chemical mechanical planarization (CMP) steps has increased and, with it, a greater need for within-wafer uniformity and wafer-to-wafer control of the thin... » read more

Changes In Auto Architectures
By Ed Sperling - 04 Aug, 2021 - Comments: 0

Automotive architectures are changing from a driver-centric model to one where technology supplements and in some cases replaces the driver. Hans Adlkofer, senior vice president and head of the Automotive Systems Group at Infineon, looks at the different levels of automation in a vehicle, what’s involved in the shift from domain to zonal architectures, why a mix of processors will be required... » read more

Graphene-based PUFs that are reconfigurable and resilient to ML attacks
By Technical Paper Link - 27 Jul, 2021 - Comments: 0

Researchers at Pennsylvania State University propose using graphene to create physically unclonable functions (PUFs) that are energy efficient, scalable, and secure against AI attacks. Abstract "Graphene has a range of properties that makes it suitable for building devices for the Internet of Things. However, the deployment of such devices will also likely require the development of s... » read more

New Power, Performance Options At The Edge
By Heidi Elliott - 21 Jul, 2021 - Comments: 0

Increasing compute intelligence at the edge is forcing chip architects to rethink how computing gets partitioned and prioritized, and what kinds of processing elements and memory configurations work best for a particular application. Sending raw data to the cloud for processing is both time- and resource-intensive, and it's often unnecessary because most of the data collected by a growing nu... » read more

Safe And Robust Machine Learning
By Ed Sperling - 15 Jul, 2021 - Comments: 0

Deploying machine learning in the real world is a lot different than developing and testing it in a lab. Quenton Hall, AI systems architect at Xilinx, examines security implications on both the inferencing and training side, the potential for disruptions to accuracy, and how accessible these models and algorithms will be when they are used at the edge and in the cloud. This involves everything ... » read more

IC Data Hot Potato: Who Owns And Manages It?
By Susan Rambo And Ed Sperling - 06 Jul, 2021 - Comments: 1

Modern inspection, metrology, and test equipment produces a flood of data during the manufacturing and testing of semiconductors. Now the question is what to do with all of that data. Image resolutions in inspection and metrology have been improving for some time to deal with increased density and smaller features, creating a downstream effect that has largely gone unmanaged. Higher resoluti... » read more

Debug: The Schedule Killer
By Brian Bailey - 30 Jun, 2021 - Comments: 0

Debug often has been labeled the curse of management and schedules. It is considered unpredictable and often can happen close to the end of the development cycle, or even after – leading to frantic attempts at work-arounds. And the problem is growing. "Historically, about 40% of time is spent in debug, and that aspect is becoming more complex," says Vijay Chobisa, director of product manag... » read more

Manufacturing Bits: June 29
By Mark LaPedus - 29 Jun, 2021 - Comments: 0

Speeding up ALD with AI The U.S. Department of Energy’s (DOE) Argonne National Laboratory has developed various ways to make atomic layer deposition (ALD) more efficient by using artificial intelligence (AI). ALD is a deposition technique that deposits materials one layer at a time on chips. For years, ALD has been used for the production of DRAMs, logic devices and other products. In ... » read more

Architectural Considerations For AI
By Brian Bailey - 24 Jun, 2021 - Comments: 2

Custom chips, labeled as artificial intelligence (AI) or machine learning (ML), are appearing on a weekly basis, each claiming to be 10X faster than existing devices or consume 1/10 the power. Whether that is enough to dethrone existing architectures, such as GPUs and FPGAs, or whether they will survive alongside those architectures isn't clear yet. The problem, or the opportunity, is that t... » read more

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