New Security Risks Create Need For Stealthy Chips


Semiconductors are becoming more vulnerable to attacks at each new process node due to thinner materials used to make these devices, as well as advances in equipment used to simulate how those chips behave. Thinner chips are now emitting light, electromagnetic radiation and various other types of noise, which can be observed using infrared and acoustic sensors. In addition, more powerful too... » read more

Where Is The Edge?


Mike Fitton, senior director of strategic planning at Achronix, talks about what the edge will look like, how that fits in with the cloud, what the requirements are both for processing and for storage, and how this concept will evolve. » read more

The Great Data Flood Ahead


The number of devices connected to the Internet is expected to exceed 1 trillion devices over the next decade or so. The timeline is a bit fuzzy, in part because no one is actually counting all of these devices, but the implications are pretty clear. A data deluge of biblical proportions is headed our way, and so far no one has any idea of what to do with all of it. From a system-level s... » read more

Solving The Memory Bottleneck


Chipmakers are scrambling to solve the bottleneck between processor and memory, and they are turning out new designs based on different architectures at a rate no one would have anticipated even several months ago. At issue is how to boost performance in systems, particularly those at the edge, where huge amounts of data need to be processed locally or regionally. The traditional approach ha... » read more

FPGA Design Tradeoffs Getting Tougher


FPGAs are getting larger, more complex, and significantly harder to verify and debug. In the past, FPGAs were considered a relatively quick and simple way to get to market before committing to the cost and time of developing an ASIC. But today, both FPGAs and eFPGAs are being used in the most demanding applications, including cloud computing, AI, machine learning, and deep learning. In some ... » read more

Machine Learning Inferencing At The Edge


Ian Bratt, fellow in Arm's machine learning group, talks about why machine learning inferencing at the edge is so difficult, what are the tradeoffs, how to optimize data movement, how to accelerate that movement, and how it differs from developing other types of processors. » read more

Improving Quality Through Data Analytics


Doug Elder, vice president and general manager at OptimalPlus, explains how to utilize data to improve reliability, how it applies to different manufacturing processes, and what happens when that data is made available to more people within an organization. » read more

The Race For Better Computational Software


Anirudh Devgan, president of Cadence, sat down with Semiconductor Engineering to talk about computational software, why it's so critical at the edge and in AI systems, and where the big changes are across the semiconductor industry. What follows are excerpts of that conversation. SE: There is no consistent approach to how data will be processed at the edge, in part because there is no consis... » read more

In Memory And Near-Memory Compute


Steven Woo, Rambus fellow and distinguished inventor, talks about the amount of power required to store data and to move it out of memory to where processing is done. This can include changes to memory, but it also can include rethinking compute architectures from the ground up to achieve up to 1 million times better performance in highly specialized systems. Related Find more memor... » read more

Edge Complexity To Grow For 5G


Edge computing is becoming as critical to the success of 5G as millimeter-wave technology will be to the success of the edge. In fact, it increasingly looks as if neither will succeed without the other. 5G networks won’t be able to meet 3GPP’s 4-millisecond-latency rule without some layer to deliver the data, run the applications and broker the complexities of multi-tier Internet apps ac... » read more

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