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Week In Review: Design, Low Power


Synopsys added MIPI C-PHY/D-PHY IP for a range of FinFET processes, adding to its MIPI camera and display IP portfolio. The C-PHY/D-PHY meet functional safety and reliability requirements of automotive ADAS and infotainment applications. It supports low-power state modes and delivers below 1.3pJ/bit at 24 Gb/s. The IP enables 4K and beyond displays and 100-megapixel cameras with support for up ... » read more

Blog Review: April 1


Rambus' Steven Woo takes an in-depth look at on-chip memory for high performance AI applications and explores some of the primary differences between HBM and GDDR6. Synopsys' Taylor Armerding warns of the risks of legacy vulnerabilities, where software has problems that were never fixed then forgotten about or never discovered in the first place, and key steps for finding and addressing them... » read more

Power/Performance Bits: March 31


Tellurium transistors Researchers from Purdue University, Washington University in St Louis, University of Texas at Dallas, and Michigan Technological University propose the rare earth element tellurium as a potential material for ultra-small transistors. Encapsulated in a nanotube made of boron nitride, tellurium helps build a field-effect transistor with a diameter of two nanometers. â... » read more

Week In Review: Design, Low Power


Tools & IP Synopsys debuted VIP and a UVM source code test suite for IP supporting Ethernet 800G. The VIP supports DesignWare 56G Ethernet, 112G Ethernet, and 112G USR/XSR PHYs for FinFET processes, which can be integrated for 800G implementations based on 8 lane x 100 Gb/s technology. The VIP can switch speed configurations dynamically at run time and includes a customizable set of frame ... » read more

Blog Review: March 25


Rambus' Steven Woo checks out common memory systems that are used in the highest performance AI applications and points to the differences between on-chip memory, HBM, and GDDR. Mentor's Colin Walls considers whether software for embedded systems should be delivered as a binary library or source code and warns of some key potential issues when requesting source code. A Synopsys writer poi... » read more

Power/Performance Bits: March 24


Backscatter Wi-Fi radio Engineers at the University of California San Diego developed an ultra-low power Wi-Fi radio they say could enable portable IoT devices. Using 5,000 times less power than standard Wi-Fi radios, the device consumes 28 microwatts while transmitting data at a rate of 2 megabits per second over a range of up to 21 meters. "You can connect your phone, your smart devices, ... » read more

Week In Review: Design, Low Power


Silicon Labs will acquire Redpine Signals' Wi-Fi and Bluetooth business, development center in Hyderabad, India, and extensive patent portfolio for $308 million in cash. Silicon Labs says the acquisition will expand the company's IoT wireless technology, including smart phone and industrial IoT, and accelerate its roadmap for Wi-Fi 6. The deal is expected to close in the second quarter of 2020.... » read more

Blog Review: March 18


Arm's Divya Prasad investigates whether power rails that are buried below the BEOL metal stack and back-side power delivery can help alleviate some of the major physical design challenges facing 3nm nodes and beyond. Rambus' Steven Woo takes a look at a Roofline model for analyzing machine learning applications that illustrates how AI applications perform on Google’s tensor processing unit... » read more

Power/Performance Bits: March 17


MRAM speed Researchers at ETH Zurich and Imec investigated exactly how quickly magnetoresistive RAM (MRAM) can store data. In the team's MRAM, electrons with opposite spin directions are spatially separated by the spin-orbit interaction, creating an effective magnetic field that can be used to invert the direction of magnetization of a tiny metal dot. "We know from earlier experiments, i... » read more

Week In Review: Design, Low Power


Tools & IP Synopsys revealed DSO.ai (Design Space Optimization AI), an autonomous AI application that searches for optimization targets in very large solution spaces of chip design, inspired by the process of DeepMind's game-playing AlphaZero. DSO.ai engines ingest large data streams generated by chip design tools and use them to explore search spaces, observing how a design evolves over t... » read more

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