tag: AI

Hidden Costs In Faster, Low-Power AI Systems
By Ed Sperling - 20 Jan, 2021 - Comments: 1

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

Is Computing Facing An Energy Crisis?
By Rob Aitken - 14 Jan, 2021 - Comments: 1

Is the end near? If the topic is energy efficiency gains in computing, the answer depends on whom you ask. The steady increase in performance per watt over the decades has been one of the most important drivers in our industry. Last year I was thumbing through a neighbor’s 1967 Motorola IC catalog that featured such space age wonders as a small control chip of the sort that went into th... » read more

Why AI Systems Are So Hard To Predict
By Ed Sperling - 12 Jan, 2021 - Comments: 0

AI can do many things, but how to ensure that it does the right things is anything but clear. Much of this stems from the fact that AI/ML/DL systems are built to adapt and self-optimize. With properly adjusted weights, training algorithms can be used to make sure these systems don't stray too far from the starting point. But how to test for that, in the lab, the fab and in the field is far f... » read more

Top Tech Videos Of 2020
By Linda Christensen - 30 Dec, 2020 - Comments: 0

2020 shaped up to be a year of major upheaval, emerging markets and even increased demand in certain sectors. So it's not surprising that videos focusing on AI, balancing power and performance, designing and manufacturing at advanced nodes, advanced packaging, and automotive-related subjects were the most popular. Of the 68 videos published this year, the following were the most viewed in ea... » read more

5 Predictions For AI Innovation In 2021
By Arun Venkatachar - 22 Dec, 2020 - Comments: 0

By Arun Venkatachar and Stelios Diamantidis Artificial intelligence (AI) has emerged as one of the most important watchwords in all of technology. The once-utopian vision of developing machines that can think and behave like humans is becoming more of a reality as engineering innovations enable the performance required to process and interpret previously unimaginable amounts of data efficien... » read more

A Collaborative Data Model For AI/ML In EDA
By Si2 - 21 Dec, 2020 - Comments: 0

This work explores industry perspectives on: Machine Learning and IC Design Demand for Data Structure of a Data Model A Unified Data Model: Digital and Analog examples Definition and Characteristics of Derived Data for ML Applications Need for IP Protection Unique Requirements for Inferencing Models Key Analysis Domains Conclusions and Proposed Future Work Abstra... » read more

Regaining U.S. Chip Competitiveness
By Mark LaPedus - 17 Dec, 2020 - Comments: 0

In the IC industry, companies compete in a multitude of different markets. At the same time, there is competition among nations on several different fronts. In technology, for example, various nations are competing for supremacy in 5G, AI and quantum computing. China has rekindled the worldwide competition in semiconductors. Backed by $150 billion in funding, the country is developing its do... » read more

AI And High-NA EUV At 3/2/1nm
By Mark LaPedus - 16 Dec, 2020 - Comments: 1

Semiconductor Engineering sat down to discuss lithography and photomask issues with Bryan Kasprowicz, director of technology and strategy and a distinguished member of the technical staff at Photronics; Harry Levinson, principal at HJL Lithography; Noriaki Nakayamada, senior technologist at NuFlare; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. To vie... » read more

Power Models For Machine Learning
By Brian Bailey - 14 Dec, 2020 - Comments: 1

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

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

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