Blog Review: April 17


In a video, Mentor's Colin Walls digs into power management in embedded software with a particular look at the Power Pyramid model. Synopsys' Taylor Armerding checks out the state of application security at this year's RSA and finds that while organizations are paying attention to security through training and dedicated teams, roadblocks still remain. Cadence's Paul McLellan considers how... » read more

The Weather Report: 2018 Study On IC/ASIC Verification Trends


Nobel Laureate Bob Dylan observed, “You don’t need a weatherman to know which way the wind blows.” Similarly, we can get a feeling for where our industry is going by attending to the flow of thought at conferences, on line, or in our daily business. But that gives us only a small window to observe the hurricane-like forces of the very large and complicated, extremely dynamic global semico... » read more

Target: 50% Reduction In Memory Power


Memory consumes about 50% or more of the area and about 50% of the power of an SoC, and those percentages are likely to increase. The problem is that static random access memory (SRAM) has not scaled in accordance with Moore's Law, and that will not change. In addition, with many devices not chasing the latest node and with power becoming an increasing concern, the industry must find ways to... » read more

Optimization Challenges For Safety And Security


Complexity challenges long-held assumptions. In the past, the semiconductor industry thought it understood performance/area tradeoffs, but over time it became clear this is not so simple. Measuring performance is no longer an absolute. Power has many dimensions including peak, average, total energy and heat, and power and function are tied together. Design teams are now dealing with the impl... » read more

Finding Code Problems Before High-Level Synthesis


In order to significantly speed up verification and to handle complex algorithms that change daily, many companies are turning to a High-Level Synthesis (HLS) methodology. But, it is extremely important that the high-level C++ model is correct. In addition, the C++ language has ambiguities that can be tough to catch during simulation. Even if correctly written, the high-level model could be cod... » read more

From AI Algorithm To Implementation


Semiconductor Engineering sat down to discuss the role that EDA has in automating artificial intelligence and machine learning with Doug Letcher, president and CEO of Metrics; Daniel Hansson, CEO of Verifyter; Harry Foster, chief scientist verification for Mentor, a Siemens Business; Larry Melling, product management director for Cadence; Manish Pandey, Synopsys fellow; and Raik Brinkmann, CEO ... » read more

Blog Review: April 10


Arm's Paul Whatmough discusses the growing use of real-time computer vision on mobile devices and proposes transfer learning as a way to enable neural network workloads on resource-constrained hardware. Cadence's Anton Klotz highlights a collaboration with Imec and TU Eindhoven on cell-aware test that reduces defect simulation time by filtering out defects with equivalent fault effects. M... » read more

Racing To The Edge


The race is on to win a piece of the edge, despite the fact that there is no consistent definition of where the edge begins and ends or how the various pieces will be integrated or ultimately tested. The edge concept originated with the Internet of Things, where the initial idea was that tens of billions of dumb sensors would communicate through gateways to the cloud. That idea persisted unt... » read more

New Approaches To Security


Different approaches are emerging to identify suspicious behavior and shut down potential breaches before they have a chance to do serious damage. This is becoming particularly important in markets where safety is an issue, and in AI and edge devices where the rapid movement of data is essential. These methods are a significant departure from the traditional way of securing devices through l... » read more

How To Manage DFT For AI Chips


Semiconductor companies are racing to develop AI-specific chips to meet the rapidly growing compute requirements for artificial intelligence (AI) systems. AI chips from companies like Graphcore and Mythic are ASICs based on the novel, massively parallel architectures that maximize data processing capabilities for AI workloads. Others, like Intel, Nvidia, and AMD, are optimizing existing archite... » read more

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