Author's Latest Posts


Software-Defined Vehicles


Automobiles long ago stopped being purely mechanical systems. But as more components are electrified — and, in particular, as the drivetrain is electrified — cars are becoming software-defined vehicles. Some think of such cars as computers on wheels. But as these systems continue to evolve, adding in more assisted and semi-autonomous capabilities, that comparison is looking increasingly ... » read more

All-in-One Vs. Point Tools For Security


Security remains an urgent concern for builders of any system that might tempt attackers, but designers find themselves faced with a bewildering array of security options. Some of those are point solutions for specific pieces of the security puzzle. Others bill themselves as all-in-one, where the whole puzzle filled in. Which approach is best depends on the resources you have available and y... » read more

Wireless Power Market Heats Up


The wireless power market is in flux as established technologies meet newer approaches. Old standards battles have simmered somewhat, but competing messages remain. What the public ends up using will depend heavily on public charging infrastructure, but the stakes are significant. The market for battery chargers is forecast to reach $25B by 2022. Most of those chargers plug into the wall, bu... » read more

What Happened To Execute-in-Place?


Executing code directly from non-volatile memory, where it is stored, greatly simplifies compute architectures — especially for simple embedded devices like microcontrollers (MCUs). However, the divergence of memory and logic processes has made that nearly impossible today. The term “execute-in-place,” or ”XIP,” originated with the embedded NOR memory in MCUs that made XIP viable. ... » read more

5G Brings New Testing Challenges


As 5G nears commercial reality, makers of chips and systems that will support 5G will need to take on the standard burden of characterizing and testing their systems to ensure both performance and regulatory adherence. Millimeter-wave (mmWave) and beamforming capabilities present the biggest testing challenges. “5G is expected to have the extended coverage plus the bandwidth to harness ... » read more

WiFi Evolves For The IoT


WiFi is everywhere, and it’s the most prevalent of the communication protocols that use unlicensed spectrum. But as a common protocol for the Internet of Things (IoT), it faces challenges both because of congestion and the amount of energy it consumes. Two new approaches aim to address those concerns. One is to use multiple channels at once. The second involves the new 802.11ah HaLow stand... » read more

What’s After 5G


This year’s IEEE Symposia on VLSI Technology and Circuits (VLSI 2020) included a presentation by NTT Docomo that looked far into the future of cellular communications, setting the stage for a broad industry shift in communication. This is far from trivial. 5G only just recently entered the commercial world, and — especially with the higher millimeter-wave (mmWave) frequencies — it has ... » read more

Moving Data And Computing Closer Together


The speed of processors has increased to the point where they often are no longer the performance bottleneck for many systems. It's now about data access. Moving data around costs both time and power, and developers are looking for ways to reduce the distances that data has to move. That means bringing data and memory nearer to each other. “Hard drives didn't have enough data flow to cr... » read more

Advanced Packaging Makes Testing More Complex


The limits of monolithic integration, together with advances in chip interconnect and packaging technologies, have spurred the growth of heterogeneous advanced packaging where multiple dies are co-packaged using 2.5D and 3D approaches. But this also raises complex test challenges, which are driving new standards and approaches to advanced-package testing. While many of the showstopper issues... » read more

Are Better Machine Training Approaches Ahead?


We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

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