tag: machine learning

How Dynamic Hardware Efficiently Solves The Neural Network Complexity Problem
By Sam Fuller - 02 Sep, 2021 - Comments: 0

Given the high computational requirements of neural network models, efficient execution is paramount. When performed trillions of times per second even the tiniest inefficiencies are multiplied into large inefficiencies at the chip and system level. Because AI models continue to expand in complexity and size as they are asked to become more human-like in their (artificial) intelligence, it is c... » read more

Why TinyML Is Such A Big Deal
By Bryon Moyer - 02 Sep, 2021 - Comments: 1

While machine-learning (ML) development activity most visibly focuses on high-power solutions in the cloud or medium-powered solutions at the edge, there is another collection of activity aimed at implementing machine learning on severely resource-constrained systems. Known as TinyML, it’s both a concept and an organization — and it has acquired significant momentum over the last year or... » read more

Stepping Up To Greater Security
By Neeraj Paliwal - 02 Sep, 2021 - Comments: 0

The stakes for security grow with each passing day. The value of our data, our devices, and our network infrastructure continually increases as does our dependence on these vital resources. Reports appear weekly, and often daily, that describe security vulnerabilities in deployments. There is a steady drumbeat of successful attacks on systems that were assumed to be protecting infrastructure, i... » read more

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

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