Speeding Up AI With Vector Instructions


A search is underway across the industry to find the best way to speed up machine learning applications, and optimizing hardware for vector instructions is gaining traction as a key element in that effort. Vector instructions are a class of instructions that enable parallel processing of data sets. An entire array of integers or floating point numbers is processed in a single operation, elim... » read more

Have Processor Counts Stalled?


Survey data suggests that additional microprocessor cores are not being added into SoCs, but you have to dig into the numbers to find out what is really going on. The reasons are complicated. They include everything from software programming models to market shifts and new use cases. So while the survey numbers appear to be flat, market and technology dynamics could have a big impact in resh... » 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

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

ML, Edge Drive IP To Outperform Broader Chip Market


The market for third-party semiconductor IP is surging, spurred by the need for more specific capabilities across a wide variety of markets. While the IP industry is not immune to steep market declines in semiconductor industry, it does have more built-in resilience than other parts of the industry. Case in point: The top 15 semiconductor suppliers were hit with an 18% decline in 2019 first-... » read more

Enabling Embedded Vision Neural Network DSPs


Neural networks are now being developed in a variety of technology segments in the embedded market, from mobile to surveillance to the automotive segment. The computational and power requirements to process this data is increasing, with new methods to approach deep learning challenges emerging every day. Vision processing systems must be designed holistically, for all platforms, with hardwa... » read more

Looking Beyond The CPU


CPUs no longer deliver the same kind of of performance improvements as in the past, raising questions across the industry about what comes next. The growth in processing power delivered by a single CPU core began stalling out at the beginning of the decade, when power-related issues such as heat and noise forced processor companies to add more cores rather than pushing up the clock frequency... » read more

AI Begins To Reshape Chip Design


Artificial intelligence is beginning to impact semiconductor design as architects begin leveraging its capabilities to improve performance and reduce power, setting the stage for a number of foundational shifts in how chips are developed, manufactured and updated in the future. AI—and machine learning and deep learning subsets—can be used to greatly improve the functional control and pow... » read more

Where ML Works Best


Anirudh Devgan, president of Cadence, sat down with Semiconductor Engineering to discuss machine learning inside and outside of EDA tools and how that will affect the future of chip and system design. What follows are excerpts of that discussion. SE: How do you see the market and use of machine learning shaping up? Devgan: There are three main areas—machine learning inside, machine lear... » read more

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