tag: quantization

Challenges For New AI Processor Architectures
By Brian Bailey - 29 Jul, 2021 - Comments: 0

Investment money is flooding into the development of new AI processors for the data center, but the problems here are unique, the results are unpredictable, and the competition has deep pockets and very sticky products. The biggest issue may be insufficient data about the end market. When designing a new AI processor, every design team has to answer one fundamental question — how much flex... » read more

Challenges Of Edge AI Inference
By Vinay Mehta - 01 Jul, 2021 - Comments: 0

Bringing convolutional neural networks (CNNs) to your industry—whether it be medical imaging, robotics, or some other vision application entirely—has the potential to enable new functionalities and reduce the compute requirements for existing workloads. This is because a single CNN can replace more computationally expensive image processing, denoising, and object detection algorithms. Howev... » read more

11 Ways To Reduce AI Energy Consumption
By Bryon Moyer - 13 May, 2021 - Comments: 1

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

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

New Ways To Optimize Machine Learning
By Bryon Moyer - 02 Apr, 2020 - Comments: 1

As more designers employ machine learning (ML) in their systems, they’re moving from simply getting the application to work to optimizing the power and performance of their implementations. Some techniques are available today. Others will take time to percolate through the design flow and tools before they become readily available to mainstream designers. Any new technology follows a basic... » read more

Analog: Avoid Or Embrace?
By Brian Bailey - 16 Jan, 2020 - Comments: 0

We live in an analog world, but digital processing has proven quicker, cheaper and easier. Moving digital data around is only possible while the physics of wires can be safely abstracted away enough to provide reliable communications. As soon as a signal passes off-chip, the analog domain reasserts control for modern systems. Each of those transitions requires a data converter. The usage ... » read more

Bridging Math And Engineering In ML
By Ed Sperling - 08 Jan, 2020 - Comments: 0

Steve Roddy, vice president of products for Arm’s Machine Learning Group, examines the intersection of high-level mathematics in the data science used in machine learning within area, speed, and power limitations, and how to bring these two worlds together with the least amount of disruption. » read more

From AI Algorithm To Implementation
By Brian Bailey - 10 Apr, 2019 - Comments: 0

Semiconductor Engineering sat down to discuss the role that EDA has in automating artificial intelligence and machine learning with Doug Letcher, president and CEO of Metrics; Daniel Hansson, CEO of Verifyter; Harry Foster, chief scientist verification for Mentor, a Siemens Business; Larry Melling, product management director for Cadence; Manish Pandey, Synopsys fellow; and Raik Brinkmann, CEO ... » read more

Building AI SoCs
By Ed Sperling - 25 Oct, 2018 - Comments: 0

Ron Lowman, strategic marketing manager at Synopsys, looks at where AI is being used and how to develop chips when the algorithms are in a state of almost constant change. That includes what moves to the edge versus the data center, how algorithms are being compressed, and what techniques are being used to speed up these chips and reduce power. https://youtu.be/d32jtdFwpcE    ... » read more

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