Configuring Processors In The Field


The convergence of two technologies, extensible processors and embedded FPGAs, is enabling the creation of processors that can be dynamically configured in the field. But it's not clear if there is a need for them or how difficult would it be to program them. This remains an open question even though there is evidence of its usefulness in the past and new products are expected to reach the mark... » read more

Implementing Strong Security For AI/ML Accelerators


A number of critical security vulnerabilities affecting high-performance CPUs identified in recent years have rocked the semiconductor industry. These high-profile vulnerabilities inadvertently allowed malicious programs to access sensitive data such as passwords, secret keys and other secure assets. The real-world risks of silicon complexity The above-mentioned vulnerabilities are primaril... » read more

Understanding SLAM (Simultaneous Localization And Mapping)


Amol Borkar, senior product manager for AI and computer vision at Cadence, talks with Semiconductor Engineering about mapping and tracking the movement of an object in a scene, how to identify key corners in a frame, how probabilities of accuracy fit into the picture, how noise can affect that, and how to improve the performance and reduce power in these systems. » read more

Simultaneous Localization And Mapping


Amol Borkar, senior product manager at Cadence, explains how to track the movement of an object in a scene and how to match features from one image to the next using SLAM. The technology is used in everything from mobile phones to automotive and drones. » read more

Bolstering Security For AI Applications


Hardware accelerators that run sophisticated artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly prevalent in data centers and endpoint devices. As such, protecting sensitive and lucrative data running on AI hardware from a range of threats is now a priority for many companies. Indeed, a determined attacker can either manipulate or steal training data, inf... » read more

Memory Subsystems In Edge Inferencing Chips


Geoff Tate, CEO of Flex Logix, talks about key issues in a memory subsystem in an inferencing chip, how factors like heat can affect performance, and where these kinds of chips will be used. » read more

The New CXL Standard


Gary Ruggles, senior staff product marketing manager at Synopsys, digs into the new Compute Express Link standard, why it’s important for high bandwidth in AI/ML applications, where it came from, and how to apply it in current and future designs. » read more

Nvidia’s Top Technologists Discuss The Future Of GPUs


Semiconductor Engineering sat down to discuss the role of the GPU in artificial intelligence, autonomous and assisted driving, advanced packaging and heterogeneous architectures with Bill Dally, Nvidia’s chief scientist, and Jonah Alben, senior vice president of Nvidia’s GPU engineering, at IEEE’s Hot Chips 2019 conference. What follows are excerpts of that conversation. SE: There are ... » read more

The Race For Better Computational Software


Anirudh Devgan, president of Cadence, sat down with Semiconductor Engineering to talk about computational software, why it's so critical at the edge and in AI systems, and where the big changes are across the semiconductor industry. What follows are excerpts of that conversation. SE: There is no consistent approach to how data will be processed at the edge, in part because there is no consis... » read more

Surround And Conquer


The processor wars are back in full swing, this time with some new players in the field. But what defines winning this time around is far less obvious than it was in the past, and it will take years before we know the outcome. The strategy is the same, though, and it's one that has been in use for years in the tech world. It began in the 1990s, when IBM came to the realization that it could ... » read more

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