tag: machine learning

HBM2E Raises The Bar For Memory Bandwidth
By Rambus - 10 Nov, 2021 - Comments: 0

AI/ML training capabilities are growing at a rate of 10X per year driving rapid improvements in every aspect of computing hardware and software. HBM2E memory is the ideal solution for the high bandwidth requirements of AI/ML training, but entails additional design considerations given its 2.5D architecture. Designers can realize the full benefits of HBM2E memory with the silicon-proven memory s... » read more

Enablers And Barriers For Connecting Diverse Data
By Anne Meixner - 09 Nov, 2021 - Comments: 0

More data is being collected at every step of the manufacturing process, raising the possibility of combining data in new ways to solve engineering problems. But this is far from simple, and combining results is not always possible. The semiconductor industry’s thirst for data has created oceans of it from the manufacturing process. In addition, semiconductor designs large and small now ha... » 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

Improving Medical Image Processing With AI
By Susan Rambo - 04 Nov, 2021 - Comments: 0

Machine learning is being integrated with medical image processing, one of the most useful technologies for medical diagnosis and surgery, greatly expanding the amount of useful information that can be gleaned from scan or MRI. For the most part, ML is being used to augment manual processes that medical personnel use today. While the goal is to automate many of these functions, it's not clea... » 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

Competing Auto Sensor Fusion Approaches
By Bryon Moyer - 07 Oct, 2021 - Comments: 0

As today’s internal-combustion engines are replaced by electric/electronic vehicles, mechanical-system sensors will be supplanted by numerous electronic sensors both for efficient operation and for achieving various levels of autonomy. Some of these new sensors will operate alone, but many prominent ones will need their outputs combined — or “fused” — with the outputs of other sensor... » read more

Fan-Out And Packaging Challenges
By Mark LaPedus - 06 Oct, 2021 - Comments: 2

Semiconductor Engineering sat down to discuss various IC packaging technologies, wafer-level and panel-level approaches, and the need for new materials with William Chen, a fellow at ASE; Michael Kelly, vice president of advanced packaging development and integration at Amkor; Richard Otte, president and CEO of Promex, the parent company of QP Technologies; Michael Liu, senior director of globa... » read more

Optimizing AI Systems
By Ed Sperling - 29 Sep, 2021 - Comments: 0

Inserting AI and machine learning into chips adds a whole new dimension of complexity, and creates a variety of potential problems, including deadlocks, loss of performance, and difficulty in achieving closure on many fronts. Gajinder Panesar, fellow at Siemens EDA, talks with Semiconductor Engineering about what’s changed and how to optimize these new devices and systems by monitoring them f... » read more

Software-Hardware Co-Design Becomes Real
By Brian Bailey - 27 Sep, 2021 - Comments: 1

For the past 20 years, the industry has sought to deploy hardware/software co-design concepts. While it is making progress, software/hardware co-design appears to have a much brighter future. In order to understand the distinction between the two approaches, it is important to define some of the basics. Hardware/software co-design is essentially a bottom-up process, where hardware is deve... » read more

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