Accelerating Endpoint Inferencing


Chipmakers are getting ready to debut inference chips for endpoint devices, even though the rest of the machine-learning ecosystem has yet to be established. Whatever infrastructure does exist today is mostly in the cloud, on edge-computing gateways, or in company-specific data centers, which most companies continue to use. For example, Tesla has its own data center. So do most major carmake... » read more

Speeding Up AI


Robert Blake, president and CEO of Achronix, sat down with Semiconductor Engineering to talk about AI, which processors work best where, and different approaches to accelerate performance. SE: How is AI affecting the FPGA business, given the constant changes in algorithms and the proliferation of AI almost everywhere? Blake: As we talk to more and more customers deploying new products and... » read more

Week in Review: IoT, Security, Auto


Internet of Things Paris-based Parrot Drones and five other companies were selected by the Pentagon’s Defense Innovation Unit and the U.S. Army to adapt off-the-shelf commercial drones for combat applications as part of the Army’s Short Range Reconnaissance program. SRR seeks to develop unmanned aerial vehicles that have a flight time of 30 minutes, a range of three kilometers (nearly two ... » read more

Week In Review: Design, Low Power


M&A NXP will acquire Marvell's Wi-Fi Connectivity business in an all-cash, asset transaction valued at $1.76 billion. The deal includes the Wi-Fi and Bluetooth technology portfolios and related assets; the business employs approximately 550 people worldwide. The deal is expected to close by calendar Q1 2020. Tools Cadence unveiled a data center-optimized FPGA-based prototyping system, ... » read more

Week in Review: IoT, Security, Auto


Internet of Things The Wing unit of Alphabet this summer will begin making drone deliveries in the Vuosarri district of Helsinki, Finland. The unmanned aerial vehicles will bear food and other items from Herkku Food, a gourmet market, and the Café Monami restaurant. The drones will bear deliveries of up to 3.3 pounds over distances of up to 6.2 miles. Comcast is reportedly developing an in... » read more

The Changing Landscape of Hardware-Based Verification And Software Development


As the EDA is gearing up for its biggest industry event, the Design Automation Conference (DAC), this year in Las Vegas, it is interesting to observe what is going on in hardware-based development of emulation and prototyping. The trends I had outlined after last DAC in 2018—system design, cloud, and machine learning—have only grown stronger and are causing changes in the development landsc... » read more

Power/Performance Bits: May 14


Detecting malware with power monitoring Engineers at the University of Texas at Austin and North Carolina State University devised a way to detect malware in large-scale embedded computer systems by monitoring power usage and identifying unusual surges as a warning of potential infection. The method relies on an external piece of hardware that can be plugged into the system to observe and m... » read more

Bottlenecks For Edge Processors


New processor architectures are being developed that can provide two to three orders of magnitude improvement in performance. The question now is whether the performance in systems will be anything close to the processor benchmarks. Most of these processors doing one thing very well. They handle specific data types and can accelerate the multiply-accumulate functions for algorithms by distri... » read more

Driving AI, ML To New Levels On MCUs


One of the most dramatic impacts of technology of late has been the implementation of artificial intelligence and machine learning on small edge devices, the likes of which are forming the backbone of the Internet of Things. At first, this happened through sheer engineering willpower and innovation. But as the drive towards a world of a trillion connected devices accelerates, we must find wa... » read more

Machine Learning On Arm Cortex-M Microcontrollers


Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there is an increasing interest in developing Neural Network (NN) solutions to deploy them on low-power edge devices such as the Arm Cortex-M microcontroller systems. CMSIS-NN is an open-source library of... » read more

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