Piovaccari suggests that new breed of ‘Renaissance’ Engineer required for Semiconductor Technology

These emerging and changing ecosystems will involve a broad range of techniques that are both new and developed. They will also require a fresh breed of “Renaissance” technicians with various cross-functional skill sets as opposed to extremely dedicated technicians who populate the technology workforce of today.

Alessandro Piovaccari, member of the Forbes Technology Council, is SVP Engineering and CTO at Silicon Labs, where he is accountable for the hardware and software research as well as the development of the company, focusing on wireless and IoT. Of almost 25 years of experience in RFIC design, Piovaccari is co-architect and project leader for TV RFIC. As an industry leader, he commonly gives lectures on the prospects of semiconductor technology worldwide, most recently at the 2019 VLSI Symposium in Kyoto, Japan.

Piovaccari’s research focuses on IoT endpoint systems, ranging from home automation to industrial and commercial apps. Several of these systems are resource-constrained and must be built in a highly reliable and efficient way requiring not only big teams of dedicated technicians but also Renaissance technicians with a variety of cross-functional skills. Consequently, Piovaccari anticipates that the technology workers of the future will continue to favor the Jack of all trades.

Piovaccari promotes the technological leaders of today to hire these Renaissance engineers, particularly when recruiting fresh college graduates. As per him, the best applicants have a powerful basis in the operation, production, and testing, science fundamentals, electrical engineering, statistics and transistor level.

While Piovaccari thinks that the semiconductor industry is evolving at a rate that is quicker than our capacity to learn, he recognizes that it is not completely unprecedented. He considers the semiconductor industry’s future as somewhat comparable to the birth of relativity and quantum mechanics.

Piovaccari considers indications of a reversal of the trend. He notes that AI and machine learning ideas, which until only a few years ago were regarded pure theory, are now seeing new light due to the accessibility of the huge quantity of information and computing resources required to achieve a decent level of practice.

 

 

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