ai-accelerator
(7)
Neurophos
Neurophos is an Austin-based photonics startup building an Optical Processing Unit (OPU) that uses light instead of electrons to run AI inference. Spun out of Duke University and the Metacept incubator, the company packs more than a million micron-scale metamaterial optical modulators - roughly 10,000x smaller than conventional photonic elements - onto a single chip to perform the matrix-vector multiplication at the heart of large AI models. It positions the OPU as an energy-efficient, drop-in alternative to GPUs for data-center inference, and raised a $110M Series A led by Gates Frontier in January 2026.
DEEPX
DEEPX is a South Korean fabless semiconductor startup building ultra-low-power neural processing units (NPUs) for on-device and edge AI. Founded in 2018 by former Apple chip designer Lokwon Kim, the company designs AI accelerators - led by its 25 TOPS DX-M1 chip - that run vision and generative AI models at under 5 watts, aiming to move intelligence out of the data center and into robots, cameras, factories, mobility and everyday devices. It raised an $80.5M Series C in 2024 at a $529M valuation and positions itself as a 'Physical AI infrastructure company.'
Tensordyne
Tensordyne is a Sunnyvale- and Munich-based AI hardware company building inference systems that use a hardware-native logarithmic number system to cut the cost and power of running large AI models. Formerly the computer-vision startup Recogni, it rebranded in September 2025 and in June 2026 unveiled Napier (TDN), a 3nm, air-cooled inference platform it claims delivers roughly 13x the throughput and 17x the efficiency of Nvidia's GB300 NVL72 rack. The company positions itself as a direct challenger to Nvidia in the market for profitable, high-throughput generative-AI inference.
Arago
Arago is a Paris and Palo Alto deeptech startup building a photonic AI accelerator that uses light instead of electrons to run AI workloads, targeting 10x lower energy consumption than today's leading GPUs at equivalent performance and cost while staying compatible with PyTorch and standard hardware ecosystems.
EnCharge AI
EnCharge AI is a Santa Clara semiconductor startup, spun out of Princeton University, building AI accelerator chips based on analog in-memory computing. Its charge-based architecture runs heavy AI workloads on laptops, workstations and edge devices at roughly 20x the energy efficiency of conventional GPUs. Its first product, the EN100 accelerator, delivers 200+ TOPS within an 8.25W power budget, aiming to move generative AI out of the data center and onto the devices people actually hold.
Lightelligence
Lightelligence builds photonic and optoelectronic hybrid computing hardware that moves and processes AI data with light instead of electrons. Spun out of MIT in 2017 on the back of a landmark Nature Photonics paper, the company designs silicon-photonics chips, optical interconnects, and AI accelerators aimed at the bandwidth and energy bottlenecks of modern data centers. In April 2026 it listed in Hong Kong as the world's first AI silicon-photonics chip stock, jumping roughly 400% on debut.
DreamBig Semiconductor Inc.
DreamBig Semiconductor is a San Jose-based chiplet company building open silicon platforms that scale AI networking from 800 Gbps to 12.8 Tbps. Founded by Marvell veterans, the team is selling chiplets, a SuperNIC, and a chiplet hub aimed at AI data centers, automotive, and edge compute.