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11 Ways To Reduce AI Energy Consumption


As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

Roadblocks For ML in EDA


Is EDA a suitable space for utilizing machine learning (ML)? The answer depends on a number of factors, including where exactly it is being applied, how much support there is from the industry, and whether there are demonstrable advantages. Exactly where ML will play a role has yet to be decided. Replacing existing heuristics with machine learning, for example, would require an industry-wide... » read more

Tradeoffs To Improve Performance, Lower Power


Generic chips are no longer acceptable in competitive markets, and the trend is growing as designs become increasingly heterogeneous and targeted to specific workloads and applications. From the edge to the cloud, including everything from vehicles, smartphones, to commercial and industrial machinery, the trend increasingly is on maximizing performance using the least amount of energy. This ... » read more

The Other Side Of AI System Reliability


Adding intelligence into pervasive electronics will have consequences, but not necessarily what most people expect. Nearly everything electronic these days has some sort of "smart" functionality built in or added on. This can be as simple as a smoke alarm that alerts you when the batteries are running low, a home assistant that learns your schedule and dials the thermostat up or down, or a r... » read more

Making Sure AI/ML Works In Test Systems


Artificial intelligence/machine learning is being utilized increasingly to find patterns and outlier data in chip manufacturing and test, improving the overall yield and reliability of end devices. But there are too many variables and unknowns to reliably predict how a chip will behave in the field using just AI. Today, every AI use case — whether a self-driving car or an industrial sortin... » read more

What Do Feedback Loops For AI/ML Devices Really Show?


AI/ML is being designed into an increasing number of chips and systems these days, but predicting how they will behave once they're in the field is, at best, a good guess. Typically, verification, validation, and testing of systems is done before devices reach the market, with an increasing amount of in-field data analysis for systems where reliability is potentially mission- or safety-criti... » read more

Hidden Costs In Faster, Low-Power AI Systems


Chipmakers are building orders of magnitude better performance and energy efficiency into smart devices, but to achieve those goals they also are making tradeoffs that will have far-reaching, long-lasting, and in some cases unknown impacts. Much of this activity is a direct result of pushing intelligence out to the edge, where it is needed to process, sort, and manage massive increases in da... » read more

Power Models For Machine Learning


AI and machine learning are being designed into just about everything, but the chip industry lacks sufficient tools to gauge how much power and energy an algorithm is using when it runs on a particular hardware platform. The missing information is a serious limiter for energy-sensitive devices. As the old maxim goes, you can't optimize what you can't measure. Today, the focus is on functiona... » read more

Transforming Vision Inspection With Machine Learning


How auto-manufacturers can apply ML & AI algorithms to enhance image analytics on their factory floor and to ensure higher product quality? Discover the next generation visual inspection in our new case study. In this case study , you will learn about: Current limitations of image inspection in the manufacturing industry. The O+ end-to-end solution, which brings machine learning and... » read more

Speeding Up AI With Vector Instructions


A search is underway across the industry to find the best way to speed up machine learning applications, and optimizing hardware for vector instructions is gaining traction as a key element in that effort. Vector instructions are a class of instructions that enable parallel processing of data sets. An entire array of integers or floating point numbers is processed in a single operation, elim... » read more

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