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The Silicon 60 Class of 2018 was EE Times’ 19th revision of its annual selection of startups the publication considered worth watching. Published on November 16, 2018, it captured a moment when machine-learning hardware was gaining prominence—but the 60 companies covered a much broader range of electronics technologies. It is a historical editorial roundup, not a current ranking or guide to companies’ status today.
What the Silicon 60 was—and how EE Times chose companies
EE Times framed the Silicon 60 as an editorial selection of startups with potential to affect electronics engineers and technology managers. Its scope favored companies with a substantial hardware focus, while recognizing that hardware businesses increasingly needed to offer platforms combining hardware and software. The publication also considered intended markets, financial position and investment profile, company maturity, and executive leadership. EE Times’ 2018 list marked new entrants with asterisks.
The list was not a single-sector award or a measure of investment performance. Its entries represented different technologies, markets, and stages of development; inclusion meant EE Times considered a company worth watching at that time, not that it had achieved commercial success or would do so.
Why machine-learning hardware stood out in 2018
EE Times counted 15 Silicon 60 companies pursuing machine learning in the 2018 edition, compared with six in the preceding version. Peter Clarke’s companion analysis presented this as evidence of machine learning’s rise as hardware-supported computing, while emphasizing that the list extended far beyond AI processors. Clarke’s analysis covers that shift in the context of the wider electronics landscape.
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The companies worked across semiconductor manufacturing, conductive materials and metamaterials, analog and digital integrated circuits, systems-on-chip, memory, FPGA fabrics, gallium nitride, energy harvesting, signal processing, 5G communications, LiDAR, wireless power, environmental sensors, MEMS, cloud-based EDA, OLED and micro-LED displays, neural networks, and vision and cognitive processing. That breadth matters: reading the 2018 class as simply an AI-chip list would miss much of its stated remit.
What the 2018 figures say—and what they do not
Clarke’s November 2018 analysis reported several figures that help explain the environment around the list. They are historical, publication-attributed figures, not current company counts or investment data.
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| Figure | What it describes |
|---|---|
| 15, up from six | Companies pursuing machine learning in the 2018 Silicon 60, compared with the previous edition, according to EE Times. |
| US$1.6 billion in 2017; US$1.3 billion in 2016; US$820 million in 2015 | Semiconductor-startup funding figures attributed to CB Insights by EE Times in 2018. |
| 32 U.S. companies, 29 headquartered in California | The geographic count for the 60-company class, as reported by EE Times in 2018. |
| About 3.5 years | The average startup age in the 2018 class, as reported by EE Times. |
| 455 companies | The cumulative number included across Silicon 60 lists since the first version in April 2004, according to the 2018 article. |
The funding values describe semiconductor startups across the specified years; they are not the amount raised by the Silicon 60 companies alone. Likewise, the geography and age figures describe EE Times’ 2018 roundup, not the semiconductor startup ecosystem now.
How different the companies and approaches were
The representative entries show why the class cannot be reduced to one product category or business model. The descriptions below reflect how the companies were presented in 2018 and do not establish present-day availability or operating status.
- AccelerComm, based in Southampton, U.K., was described as a semiconductor IP-core company developing polar encoder and decoder solutions for 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 and described its MLU100 processor and 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; the company also announced its selection on November 17, 2018. Prophesee’s announcement is company-published corroboration.
The broader list also named Graphcore’s machine-learning processor effort, Groq’s cognitive-computing chip plans, and Gyrfalcon’s Lightspeeur AI processor, alongside sensor, memory, MEMS, and display startups. Those descriptions are reports of 2018 efforts and plans, rather than evidence of current products or capabilities.
Technology trade-offs behind the roundup
Clarke’s 2018 analysis contrasted digital and analog approaches to computing. Digital programmable processors can be more flexible and compatible across applications, while potentially using more power. Analog approaches may offer energy-efficiency advantages but can be more application-specific. These are broad trade-offs as described in that historical analysis, not a current assessment of any named company or a universal rule for every design.
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The companies also targeted very different contexts: edge devices and sensors, communications and automotive systems, industrial uses, displays, and data-center computing. A startup’s inclusion therefore says little on its own about how its technology compares with another entrant’s; the intended workload and market matter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to interpret the list today
Use the Silicon 60 Class of 2018 as a snapshot of what EE Times considered notable in the electronics startup landscape at the time. It documents the growing attention to machine-learning hardware while preserving a view of the wider mix of semiconductor, sensing, communications, display, and design-tool innovation.
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- It can be a gift option
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- Its selection and company descriptions are editorial judgments from 2018, not a present-day ranking.
- The funding, geography, company-age, and cumulative-list figures belong to the 2018 reporting and should retain their dates and attribution.
- The list alone does not establish whether a company still operates, whether a product reached market, or what is available now.
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