tag: Mentor

Making Tradeoffs With AI/ML/DL
By Ann Mutschler - 11 May, 2023 - Comments: 0

Machine learning, deep learning, and AI increasingly are being used in chip design, and they are being used to design chips that are optimized for ML/DL/AI. The challenge is understanding the tradeoffs on both sides, both of which are becoming increasingly complex and intertwined. On the design side, machine learning has been viewed as just another tool in the design team's toolbox. That's s... » read more

Rethinking Engineering Education In The U.S.
By Karen Heyman - 11 May, 2023 - Comments: 8

The CHIPS Act, as well as the ongoing need for talent, is causing both industry and academia in America to rethink engineering education, resulting in new approaches and stronger partnerships. As an example, Arizona State University (ASU) now has a Secure, Trusted, and Assured Microelectronics Center (STAM). The center offers an interdisciplinary approach to learning secure and trusted semic... » read more

Conquer Placement And Clock Tree Challenges In HPC Designs
By Janet Attar - 11 May, 2023 - Comments: 0

High-performance computing (HPC) applications require IC designs with maximum performance. However, as process technology advances, achieving high performance has become increasingly challenging. Designers need digital implementation tools and methodologies that can solve the thorny issues in HPC designs, including placement and clock tree challenges. Placement and clock tree synthesis are c... » read more

Placement And CTS Techniques For High-Performance Computing Designs
By Siemens EDA - 10 May, 2023 - Comments: 0

This paper discusses the challenges of designing high-performance computing (HPC) integrated circuits (ICs) to achieve maximum performance. The design process for HPC ICs has become more complex with each new process technology, requiring new architectures and transistors. We highlight how the Siemens Aprisa digital implementation solution can solve placement and clock tree challenges in HPC de... » read more

Blog Review: May 10
By Jesse Allen - 10 May, 2023 - Comments: 0

Synopsys' Alessandra Nardi and Uyen Tran explain how to meet quality, reliability, functional safety, and security requirements of automotive chips through thorough test programs, path-margin monitoring, and design failure mode and effect analysis (DFMEA). Cadence's Veena Parthan explores how computational fluid dynamics can help predict and model the generation, propagation, and mitigation ... » read more

Designing Crash-Proof Autonomous Vehicles
By John Koon - 10 May, 2023 - Comments: 0

Autonomous vehicles keep crashing into things, even though ADAS technology promises to make driving safer because machines can think and react faster than human drivers. Humans rely on seeing and hearing to assess driving conditions. When drivers detect objects in front of the vehicle, the automatic reaction is to slam on the brakes or swerve to avoid them. Quite often drivers cannot react q... » read more

Pinpointing Timing Delays Can Improve Chip Reliability
By Anne Meixner - 09 May, 2023 - Comments: 0

Growing pressure to improve IC reliability in safety- and mission-critical applications is fueling demand for custom automated test pattern generation (ATPG) to detect small timing delays, and for chip telemetry circuits that can assess timing margin over a chip's lifetime. Knowing the timing margin in signal paths has become an essential component in that reliability. Timing relationships a... » read more

3D Structures Challenge Wire Bond Inspection
By Karen Heyman - 09 May, 2023 - Comments: 1

Adding more layers in packages is making it difficult, and sometimes impossible, to inspect wire bonds that are deep within the different layers. Wire bonds may seem like old technology, but it remains the bonding approach of choice for a broad swath of applications. This is particularly evident in automotive, industrial, and many consumer applications, where the majority of chips are not de... » read more

ML Automotive Chip Design Takes Off
By Ann Mutschler - 04 May, 2023 - Comments: 0

Machine learning is increasingly being deployed across a wide swath of chips and electronics in automobiles, both for improving reliability of standard parts and for the creation of extremely complex AI chips used in increasingly autonomous applications. On the design side, the majority of EDA tools today rely on reinforcement learning, a machine learning subset of AI that teaches a machine ... » read more

Hardware-Based Cybersecurity For Software-Defined Vehicles
By Lee Harrison - 04 May, 2023 - Comments: 0

As vehicle technology advances, so does the complexity of the electrical/electronic systems within these smart vehicles. A software-defined vehicle (SDV) relies on centralized compute and an advanced software stack to control most of its functionality, from engine performance to infotainment systems. SDVs are becoming more important as automakers look to improve vehicle performance, reduce emis... » read more

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