tag: ML

Bandwidth Utilization Side-Channel On ML Inference Accelerators
By Arm - 10 Nov, 2021 - Comments: 0

Abstract—Accelerators used for machine learning (ML) inference provide great performance benefits over CPUs. Securing confidential model in inference against off-chip side-channel attacks is critical in harnessing the performance advantage in practice. Data and memory address encryption has been recently proposed to defend against off-chip attacks. In this paper, we demonstrate that bandwidth... » read more

Solving Real World AI Productization Challenges With Adaptive Computing
By Nick Ni - 08 Nov, 2021 - Comments: 0

The field of artificial intelligence (AI) moves swiftly, with the pace of innovation only accelerating. While the software industry has been successful in deploying AI in production, the hardware industry – including automotive, industrial, and smart retail – is still in its infancy in terms of AI productization. Major gaps still exist that hinder AI algorithm proof-of-concepts (PoC) from b... » read more

Getting Better Edge Performance & Efficiency From Acceleration-Aware ML Model Design
By Vlad Bronstein - 04 Nov, 2021 - Comments: 0

The advent of machine learning techniques has benefited greatly from the use of acceleration technology such as GPUs, TPUs and FPGAs. Indeed, without the use of acceleration technology, it’s likely that machine learning would have remained in the province of academia and not had the impact that it is having in our world today. Clearly, machine learning has become an important tool for solving... » read more

Easier And Faster Ways To Train AI
By Bryon Moyer - 04 Nov, 2021 - Comments: 0

Training an AI model takes an extraordinary amount of effort and data. Leveraging existing training can save time and money, accelerating the release of new products that use the model. But there are a few ways this can be done, most notably through transfer and incremental learning, and each of them has its applications and tradeoffs. Transfer learning and incremental learning both take pre... » read more

ML-based Routing Congestion And Delay Estimation In Vivado ML Edition
By Xilinx - 03 Nov, 2021 - Comments: 0

The FPGA physical design flow offers a compelling opportunity for Machine Learning for CAD (MLCAD) for the following reasons: • An ML solution can be applied wholesale to a device family. • There is a vast data farm that can be harvested from device models and design data from broad applications. • There is a single streamlined design flow that an be instrumented, annotated, and quer... » read more

What’s Missing For Designing Chips At The System Level
By Ed Sperling - 01 Nov, 2021 - Comments: 0

Semiconductor Engineering sat down to talk about design challenges in advanced packages and nodes with John Lee, vice president and general manager for semiconductors at Ansys; Shankar Krishnamoorthy, general manager of Synopsys' Design Group; Simon Burke, distinguished engineer at Xilinx; and Andrew Kahng, professor of CSE and ECE at UC San Diego. This discussion was held at the Ansys IDEAS co... » read more

HBM3: Big Impact On Chip Design
By Ann Mutschler - 14 Oct, 2021 - Comments: 0

An insatiable demand for bandwidth in everything from high-performance computing to AI training, gaming, and automotive applications is fueling the development of the next generation of high-bandwidth memory. HBM3 will bring a 2X bump in bandwidth and capacity per stack, as well as some other benefits. What was once considered a "slow and wide" memory technology to reduce signal traffic dela... » read more

HBM3 Memory: Break Through To Greater Bandwidth
By Rambus - 06 Oct, 2021 - Comments: 0

AI/ML’s demands for greater bandwidth are insatiable driving rapid improvements in every aspect of computing hardware and software. HBM memory is the ideal solution for the high bandwidth requirements of AI/ML training, but it entails additional design considerations given its 2.5D architecture. Now we’re on the verge of a new generation of HBM that will raise memory and capacity to new hei... » read more

EDA Vendors Widen Use Of AI
By Ed Sperling - 29 Sep, 2021 - Comments: 0

EDA vendors are widening the use of AI and machine learning to incorporate multiple tools, providing continuity and access to consistent data at multiple points in the semiconductor design flow. While gaps remain, early results from a number of EDA tools providers point to significant improvements in performance, power, and time to market. AI/ML has been deployed for some time in EDA. Still,... » read more

Microelectronics And The AI Revolution
By Frank Schirrmeister - 23 Sep, 2021 - Comments: 0

It is no secret that artificial intelligence and machine learning (AI/ML) are critical drivers for growth in electronics, and particularly, for semiconductors. The recent AI Hardware Summit showcased trends in AI/ML, both in enabling and using it in various application domains, including EDA. As part of the summit, Imec had organized a panel on “Advanced Microelectronics Technologies Driving ... » read more

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