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The Silicon 60 Class of 2018 was EE Times’ 19th annual revision of a list of 60 startups it considered worth watching. Published on November 16, 2018, it highlighted machine-learning hardware while spanning a much wider range of electronics technologies. It is a historical editorial selection—not a current ranking or guide to which companies or products are available today.
What the Silicon 60 was
EE Times presented the Silicon 60 as a selection of startups with potential impact on electronics engineers and technology managers. Its editors focused on companies with a substantial connection to hardware, while recognizing that hardware businesses increasingly needed to pair their products with software platforms. The selection also considered intended markets, financial position and investment profile, company maturity, and executive leadership.
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The 2018 feature marked new entrants with asterisks. The publication said that, since the list’s first version in April 2004, the cumulative Silicon 60 had included 455 companies. That is the total reported in the 2018 article, not a present-day count.
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Machine learning was prominent, but the list was not just about AI chips
EE Times counted 15 companies pursuing machine learning in the 2018 class, compared with six in the previous version. Peter Clarke’s companion analysis framed this as a rise in machine learning as hardware-supported computing, but the list covered much more than processors for AI.
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Its areas included semiconductor manufacturing, conductive materials and metamaterials, analog and digital ICs, systems-on-chip, memory, FPGA fabrics, gallium nitride for power and lighting, energy harvesting, low-voltage IC operation, signal processing, 5G communications, LiDAR, wireless power, environmental sensing, MEMS, cloud-based EDA, OLED and micro-LED displays, neural networks, and vision and cognitive processing. The breadth reflects EE Times’ electronics-industry lens: the list included enabling components, materials, tools, sensors, and displays alongside computing architectures.
What the 2018 figures say—and what they do not
In its November 2016 analysis, EE Times attributed semiconductor-startup fundraising estimates to CB Insights: US$1.3 billion in 2016 and US$820 million in 2015. The companion article reported US$1.6 billion for 2017. These are historical estimates as attributed by EE Times, not current investment totals or a forecast.
The 2018 analysis counted 32 U.S. companies among the 60, with 29 headquartered in California, and reported an average startup age of about 3.5 years. Those figures describe that edition of the list; they do not establish today’s geographic distribution or the current age or status of any company.
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The entries represented distinct technologies and business models rather than one uniform category of startup:
- AccelerComm, based in Southampton, U.K., was described as developing semiconductor IP cores for polar encoders and decoders used in 3GPP 5G channel coding.
- AerNos, in La Jolla, California, worked on gas and volatile organic compound sensing using doped materials and nanotechnology.
- Aledia, in Grenoble, France, described LEDs formed in gallium-nitride pillars grown on silicon wafers.
- Cambricon, in Beijing, was developing AI chips; the entry described its MLU100 processor and an intelligent processing card.
- SiFive, in San Mateo, California, offered RISC-V IP cores, processors, and boards.
- Prophesee was selected for the class for its event-based vision systems, according to the company’s November 17, 2018 announcement.
Other names in the overview included Graphcore’s machine-learning processor effort, Groq’s cognitive-computing chip plans, and Gyrfalcon’s Lightspeeur AI processor. These are descriptions and plans reported in 2018; inclusion does not verify subsequent product releases, commercial availability, or present operating status.
How the analysis contrasted hardware approaches
Clarke’s 2018 analysis drew a distinction between digital programmable approaches and analog or more application-specific ones. In his framing, digital solutions could offer flexibility and compatibility, while analog designs could have energy-efficiency advantages at the cost of greater application specificity. That is a conceptual comparison in the context of the 2018 article, not a current assessment of every listed company or technology.
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The entries also targeted very different settings, including edge devices and sensors, communications, automotive and industrial applications, data-center computing, and displays. As a result, the Silicon 60 is more useful as a snapshot of startup activity across electronics than as a direct comparison of interchangeable products.
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How to read the list today
Use the Silicon 60 Class of 2018 to understand what EE Times considered notable in electronics startups at that moment, especially the growing attention to machine-learning hardware. Do not use the feature alone as a current company directory, investment assessment, market forecast, or product recommendation. Its descriptions and selection are historical, and the cited sources do not establish the companies’ current status or product inventory.
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