After 20 years of leading technical marketing in Arrow Electronics in both Israel and across Europe, Amir Sherman has decided to leave the company to build his own business development consultancy and TinyML and Embedded-ML Technologies firm. “I have fulfilled my goals with the company”, he told Techtime. “It’s time for a new adventure.”
Amir joined Arrow Electronics Israel in 2001 as a Field Application Engineer with responsibility for microcontrollers and microprocessors, and later to become FAE Manager and Technical & Marketing Manager. In 2013, he relocated to Germany having been promoted to Supplier Business Manager for ST Microelectronics in EMEA. In Germany he had established the Embedded Group and the System On Module programme in Arrow.
In this role he was part of the team that developed more than 30 Arrow Boards and Platforms, while also helping to develop the Hardware team in Budapest and the Software team in Gdansk. The Gdansk team drives both AI and IoT activities within the company. In his last role in Arrow he was the Director of Embedded Technologies and Engineering Solutions, and also General Manager for Israel.
What are you doing now?
“At Arrow I managed several Arrow Platforms based on the popular 96Boards Community like the Dragonboard410 or Avenger96. I am now doing the same for global semiconductor companies that wish to build similar programs. I am also helping Israeli companies with specific business development requirement: introducing them to global partners and introducing semiconductor companies to local distributors and developing business plans for them. Lately, Edge Impulse has nominated me Country Manager and a Global promoter of an innovative platform that simplifies AI-ML in Embedded systems under TinyML and Embedded-ML.“
What is TinyML? Why is it so important?
“It is a fascinating technology, as it takes Embedded and IoT to the next level by adding Machine Learning at the Edge – not in the Cloud. TinyML refers to the machine learning technologies on the tiniest of microprocessors using the least possible power (usually in the mW range and below) while delivering optimum results.
“According to ABI Research, a total of 2.5 billion devices are expected to be shipped with a Tiny Machine Learning (TinyML) chipset in 2030, propelled by the increasing focus on low latency, advanced automation, and the availability of ultra-power-efficient Artificial Intelligence (AI) chipsets, also known as Very Edge AI or Embedded AI.
“These chipsets perform AI inference almost completely on board, but still continue to rely on external resources for training, such as gateways, on-premise servers, or the Cloud. Edge Impulse was designed for software developers, engineers and domain experts to solve real problems using ML on edge devices, with no need to be an expert in machine learning. The technology features cloud-based UX, extensive documentation and open source SDKs.”