tag: AI

Power/Performance Bits: June 15
By Jesse Allen - 15 Jun, 2021 - Comments: 0

Low-loss photonic IC Researchers at EPFL built a photonic integrated circuit with ultra-low loss. The team focused on silicon nitride (Si3N4), which has orders of magnitude lower optical loss compared to silicon. It is used in low-loss applications such as narrow-linewidth lasers, photonic delay lines, and nonlinear photonics. In applying the material to photonic ICs, they took advantage... » read more

Changes In Sensors And DSPs
By Ed Sperling - 14 Jun, 2021 - Comments: 0

Pulin Desai, group director for product marketing, management and business development at Cadence, talks about why processing is moving closer to the end point, how to save energy through reduced area and sensor fusion, and the impact of specialization, 3D capture and always-on circuits. » read more

Thermal Floorplanning For Chips
By Brian Bailey - 10 Jun, 2021 - Comments: 0

Heat management is becoming crucial to an increasing number of chips, and it's one of a growing number of interconnected factors that must be considered throughout the entire development flow. At the same time, design requirements are exacerbating thermal problems. Those designs either have to increase margins or become more intelligent about the way heat is generated, distributed, and dissi... » read more

Accelerating AI/ML Inferencing With GDDR6 DRAM
By Frank Ferro - 10 Jun, 2021 - Comments: 0

The origins of graphics double data rate (GDDR) memory can be traced to the rise of 3D gaming on PCs and consoles. The first graphics processing units (GPU) packed single data rate (SDR) and double data rate (DDR) DRAM – the same solution used for CPU main memory. As gaming evolved, the demand for higher frame rates at ever higher resolutions drove the need for a graphics-workload specific me... » read more

Customizing Chips For Power And Performance
By Ed Sperling - 10 Jun, 2021 - Comments: 0

Sandro Cerato, senior vice president and CTO of the Power & Sensor Systems Business Unit at Infineon Technologies, sat down with Semiconductor Engineering to talk about fundamental shifts in chip design with the rollout of the edge, AI, and more customized solutions. What follows are excerpts of that conversation. SE: The chip market is starting to fall into three distinct buckets, the e... » read more

Accelerate Adoption Of High-Speed, Low-Latency, Cache-Coherent Standards Using Formal Verification
By Cadence - 09 Jun, 2021 - Comments: 0

We continue to see huge growth in data and compute demand, fueled by increased global data traffic with the 5G rollout, the prevalence of streaming services, and expanded artificial intelligence and machine learning (AI/ML) applications. Several new industry-standard specifications have emerged in recent years to define the protocols of the underlying electronic components and IP building block... » read more

There’s More To Machine Learning Than CNNs
By Bryon Moyer - 09 Jun, 2021 - Comments: 1

Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more

Failure Mechanism Detection Algorithm With MOSFET Body Diode
By Fraunhofer IIS/EAS - 09 Jun, 2021 - Comments: 0

Autonomous driving is playing a big role in the automotive industry and defines the future of mobility on a big scale. However, autonomous driving faces several challenges, such as the performance of artificial intelligence and hardware reliability. To ensure safe functionality, the reliability of the electronic components plays an essential role and must be taken into consideration. One aspect... » read more

3 Technologies That Will Challenge Test
By Ed Sperling - 08 Jun, 2021 - Comments: 0

As chips are deployed in more complex systems and with new technologies, it's not clear exactly what chipmakers and systems vendors will be testing. The standard tests for voltage, temperature and electrical throughput still will be needed, of course. But that won't be sufficient to ensure that sensor fusion, machine learning, or millimeter wave 5/6G will be functioning properly. Each of tho... » read more

Fab Fingerprint For Proactive Yield Management
By Onto Innovation - 08 Jun, 2021 - Comments: 0

The following paper presents a case study describing how to improve yield and fab productivity by implementing a frequent pattern database that utilizes artificial intelligence-based spatial pattern recognition (SPR) and wafer process history. This is important because associating spatial yield issues with process and tools is often performed as a reactive analysis, resulting in increased wafer... » read more

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