Tracking Automotive’s Rapidly Shifting Ecosystem


The automotive ecosystem is becoming much harder to navigate as automakers, Tier 1s and IP vendors redefine their relationships based upon shifting value caused by an rapidly expanding amount of increasingly interdependent and complex electronic content. Predictions of massive change started almost a decade ago with a number of pilot programs around autonomous vehicles. But those shifts real... » read more

Week In Review: Auto, Security, Pervasive Computing


AI, machine learning Cadence says it has optimized its Tensilica HiFi digital signal processor IP to efficiently execute TensorFlow Lite for Microcontrollers, which are used in Google’s machine learning platform for edge. This means developers of AI/ML on the edge systems can now put better audio processing on edge devices with ML applications like keyword detection, audio scene detection, n... » 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

Week In Review: IoT, Security, Autos


Internet of Things Sensors that see in the dark, look deep into our faces and hear the impossible, were all part of ams’ CES lineup this week. ams announced that it has designed an advanced spectral ambient light sensor (ALS) for high-end mobile phone cameras. The ALS, called the AS7350, identifies the light source and makes an accurate white balance under low-light and other non-ideal condi... » read more

Fixed and Floating FMCW Radar Signal Processing with Tensilica DSPs


Automotive advanced driver assistance system (ADAS) applications increasingly demand radar modules with better capability and performance. These applications require sophisticated radar processing algorithms and powerful digital signal processors (DSPs) to run them. Because these embedded systems have limited power and cost budgets, the DSP’s instruction set architecture (ISA) needs to be eff... » read more

Week in Review: IoT, Security, Autos


Products/Services Rambus reports completing the sale of its Payments and Ticketing businesses to Visa for $75 million in cash. “With 30 years of experience pushing the envelope in semiconductor design, we look toward a future of continued innovation to carry on our mission of making data faster and safer,” Rambus President and CEO Luc Seraphin said in a statement. “Completing this transa... » read more

Making Sense Of ML Metrics


Steve Roddy, vice president of products for Arm’s Machine Learning Group, talks with Semiconductor Engineering about what different metrics actually mean, and why they can vary by individual applications and use cases. » read more

High-Performance DSP And Control Processing For Complex 5G Requirements


In the early 2000s, digital signal processors (DSP) were simple in architecture and limited in performance, but complex in programming. However, they evolved to meet of the increased performance requirements of 3G cellular baseband modem applications. A typical 3G modem system would have a single DSP optimized for dual/quad SIMD MAC performance with basic DSP filter instructions like Fast Fouri... » read more

Why Scaling Must Continue


The entire semiconductor industry has come to the realization that the economics of scaling logic are gone. By any metric—price per transistor, price per watt, price per unit area of silicon—the economics are no longer in the plus column. So why continue? The answer is more complicated than it first appears. This isn't just about inertia and continuing to miniaturize what was proven in t... » read more

Powering The Edge: Driving Optimal Performance With the Arm ML Processor


On-device machine learning (ML) processing is already happening in more than 4 billion smart phones. As the adoption of connected devices continues to grow exponentially, the resulting data explosion means cloud processing could soon become an expensive and high-latency luxury. The Arm ML processor is defining the future of ML inference at the edge, allowing smart devices to make independent... » read more

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