Improving AI Productivity With AI

AI is showing up or proposed for nearly all aspects of chip design, but it also can be used to improve the performance of AI chips and to make engineers more productive earlier in the design process. Matt Graham, product management group director at Cadence, talks with Semiconductor Engineering about the role of AI in identifying patterns that are too complex for the human brain to grasp, how t... » read more

Patterns And Issues In AI Chip Design

AI is becoming more than a talking point for chip and system design, taking on increasingly complex tasks that are now competitive requirements in many markets. But the inclusion of AI, along with its machine learning and deep learning subcategories, also has injected widespread confusion and uncertainty into every aspect of electronics. This is partly due to the fact that it touches so many... » read more

Pinpointing Timing Delays Can Improve Chip Reliability

Growing pressure to improve IC reliability in safety- and mission-critical applications is fueling demand for custom automated test pattern generation (ATPG) to detect small timing delays, and for chip telemetry circuits that can assess timing margin over a chip's lifetime. Knowing the timing margin in signal paths has become an essential component in that reliability. Timing relationships a... » 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

Algorithm HW Framework That Minimizes Accuracy Degradation, Data Movement, And Energy Consumption Of DNN Accelerators (Georgia Tech)

This new research paper titled "An Algorithm-Hardware Co-design Framework to Overcome Imperfections of Mixed-signal DNN Accelerators" was published by researchers at Georgia Tech. According to the paper's abstract, "In recent years, processing in memory (PIM) based mixed-signal designs have been proposed as energy- and area-efficient solutions with ultra high throughput to accelerate DNN com... » read more

Machine Learning Application For Early Power Analysis Accuracy Improvement

In this paper, we introduce a machine learning (ML) application that accurately estimates the switching power of the cells without needing the SPEF file (SPEF less PA flow). Three ML models (multi-linear regression, random forest and decision tree) were trained and tested on different industrial designs at 7nm technology. They are trained using different cells’ properties available, SPEF, and... » read more

Architecting Faster Computers

To create faster computers, the industry must take a major step back and re-examine choices that were made half a century ago. One of the most likely approaches involves dropping demands for determinism, and this is being attempted in several different forms. Since the establishment of the von Neumann architecture for computers, small, incremental improvements have been made to architectures... » read more

Gaps In The AI Debug Process

When an AI algorithm is deployed in the field and gives an unexpected result, it's often not clear whether that result is correct. So what happened? Was it wrong? And if so, what caused the error? These are often not simple questions to answer. Moreover, as with all verification problems, the only way to get to the root cause is to break the problem down into manageable pieces. The semico... » read more

Improving Accuracy In Satellite Navigation Systems

Increasing dependency on the global navigation satellite system (GNSS) constellations is raising concerns about what happens when signals are unavailable, even for short periods of time. GNSS systems affect our daily lives in ways we often don’t see, from location services to cell phone timing. In fact, these satellites have become a necessary part of critical infrastructure, and higher ac... » 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

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