tag: AI

Fast, Low-Power Inferencing
By Russell Klein - 10 Dec, 2020 - Comments: 1

Power and performance are often thought of as opposing goals, opposite sides of the same coin if you will. A system can be run really fast, but it will burn a lot of power. Ease up on the accelerator and power consumption goes down, but so does performance. Optimizing for both power and performance is challenging. Inferencing algorithms for Convolutional Neural Networks (CNN) are compute int... » read more

Standard Benchmarks For AI Innovation
By Dylan Zika - 10 Dec, 2020 - Comments: 0

There is no standard measurement for machine learning performance today, meaning there is no single answer for how companies build a processor for ML across all use cases while balancing compute and memory constraints. For the longest time, every group would pick a definition and test to suit their own needs. This lack of common understanding of performance hinders customers' buying decis... » read more

Tapping Into Purpose-Built Neural Network Models For Even Bigger Efficiency Gains
By Quenton Hall - 09 Dec, 2020 - Comments: 0

Neural networks can be categorized as a set of algorithms modelled loosely after the human brain that can ‘learn’ by incorporating new data. Indeed, many benefits can be derived from developing purpose-built “computationally efficient” neural network models. However, to ensure your model is effective, there are several key requirements that need to be considered. One critical conside... » read more

The Cyber-Industrial Revolution
By Ed Sperling - 07 Dec, 2020 - Comments: 0

Semiconductors won't save the world, but they certainly will help. In fact, it's arguable whether any significant progress will be made on such issues as global warming or future medical breakthroughs without the aid of ICs. After decades of struggling just to get chips to work at each new process node, the semiconductor industry is moving into a new phase. Processing is now almost ubiquitou... » read more

Transforming Vision Inspection With Machine Learning
By Optimal Plus - 07 Dec, 2020 - Comments: 0

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

Edge Inference Applications And Market Segmentation
By Geoff Tate - 03 Dec, 2020 - Comments: 0

Until recently, most AI was in data centers/cloud and most of that was training. Things are changing quickly. Projections are AI sales will grow rapidly to tens of billions of dollars by the mid 2020s, with most of the growth in edge AI inference. Data center/cloud vs. edge inference: What’s the difference? The data center/cloud is where inference started on Xeons. To gain efficiency, much ... » read more

Startup Funding: November 2020
By Jesse Allen - 02 Dec, 2020 - Comments: 0

Numerous chipmakers pulled in funding in November 2020, with investors putting money into interconnects, memories, AI hardware, and quantum computing. Launching from stealth was a startup aiming to combine AI and 5G. Autonomous delivery did well, too, with one company raising a massive $500M. This month, we take a look at 28 companies that raised a collective $1.1B. Semi & design Connec... » read more

Convolutional Neural Network With INT4 Optimization
By Xilinx - 01 Dec, 2020 - Comments: 0

Xilinx provides an INT8 AI inference accelerator on Xilinx hardware platforms — Deep Learning Processor Unit (XDPU). However, in some resource-limited, high-performance and low-latency scenarios (such as the resource-power-sensitive edge side and low-latency ADAS scenario), low bit quantization of neural networks is required to achieve lower power consumption and higher performance than provi... » read more

Manufacturing Bits: Dec. 1
By Mark LaPedus - 01 Dec, 2020 - Comments: 0

New phase-change materials The National Institute of Standards and Technology (NIST) has developed an open source machine learning algorithm for use in discovering and developing new materials. NIST’s technology, called CAMEO, has already been used by researchers to discover a new phase-change memory material. CAMEO, which stands for Closed-Loop Autonomous System for Materials Exploration... » read more

Forward And Backward Compatibility In IC Designs
By Ann Mutschler - 30 Nov, 2020 - Comments: 0

Future-proofing of designs is becoming more difficult due to the accelerating pace of innovation in architectures, end markets, and technologies such as AI and machine learning. Traditional approaches for maintaining market share and analyzing what should be in the next rev of a product are falling by the wayside. They are being replaced by best-guesses about market trends and a need to bala... » read more

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