tag: machine learning

Multi-Layer Processing Boosts Inference Throughput/Watt
By Geoff Tate - 15 Apr, 2019 - Comments: 0

The focus in discussion of inference throughput is often on the computations required. For example, YOLOv3, a power real time object detection and recognition model, requires 227 BILLION MACs (multiply-accumulates) to process a single 2 Mega Pixel image! This is with the Winograd Transformation; it’s more than 300 Billion without it. And there is a lot of discussion of the large size ... » read more

Low Power Meets Variability At 7/5nm
By Ann Mutschler - 11 Apr, 2019 - Comments: 0

Power-related issues are beginning to clash with process variation at 7/5nm, making timing closure more difficult and resulting in re-spins caused by unexpected errors and poor functional yield. Variability is becoming particularly troublesome at advanced nodes, and there are multiple causes of that variability. One of the key ones is the manufacturing process, which can be affected by every... » 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

Machine Learning on Arm Cortex-M Microcontrollers
By Arm - 10 Apr, 2019 - Comments: 0

Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing Neural Network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. CMSIS-NN is an open-source library of... » read more

How Do I Know? A Machine Told Me So
By Judy Davies - 09 Apr, 2019 - Comments: 0

More than 375 years ago, René Descartes wrote “I think, therefore I am.” And “Think” has been a slogan used by no less a technology giant than IBM for more than a century. The thought process has been a defining aspect of humanity since our beginning. But now technologists are working to imbue that capability into machines through artificial intelligence. Programming computers is no... » read more

Power/Performance Bits: April 8
By Jesse Allen - 08 Apr, 2019 - Comments: 0

Predicting battery life Researchers at Stanford University, MIT, and Toyota Research Institute developed a machine learning model that can predict how long a lithium-ion battery can be expected to perform. The researchers' model was trained on a few hundred million data points of batteries charging and discharging. The dataset consists of 124 commercial lithium iron phosphate/graphite cells... » read more

Combining SLAM And CNN For High-Performance Augmented Reality
By Gordon Cooper - 04 Apr, 2019 - Comments: 0

Robotics and headsets or goggles are the most common hardware devices requiring AR/VR/mixed reality, and AR is coming to mobile phones, tablets, and automobiles as well. For hardware devices to see the world around them and add to that reality with inserted graphics or images, they need to determine their position in space and map the surrounding environment. Simultaneous localization and ma... » read more

Big Shift In Multi-Core Design
By Ann Mutschler - 03 Apr, 2019 - Comments: 0

Hardware and software engineers have a long history of working independently of each other, but that insular behavior is changing in emerging areas such as AI, machine learning and automotive as the emphasis shifts to the system level. As these new markets consume more semiconductor content, they are having a big impact on the overall design process. The starting point in many of these desig... » read more

Utilizing More Data To Improve Chip Design
By Ann Mutschler - 28 Mar, 2019 - Comments: 0

Just about every step of the IC tool flow generates some amount of data. But certain steps generate a mind-boggling amount of data, not all of which is of equal value. The challenge is figuring out what's important for which parts of the design flow. That determines what to extract and loop back to engineers, and when that needs to be done in order to improve the reliability of increasingly com... » read more

The Automation Of AI
By Brian Bailey - 28 Mar, 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

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