The archive · Hardware & Devices · Technical decision · 2022-2026
EnCharge AI bets analog in-memory chips run 200 TOPS AI on 8W laptops
Princeton spinout EnCharge AI raised $100M+ led by Tiger Global to make analog in-memory accelerators that run on-device AI inference at ~20x GPU efficiency.
EnCharge AI
What the business is
Designs charge-based analog in-memory AI accelerators (EN100) that run inference on laptops, workstations, and edge devices at a fraction of a GPU's power draw.
Starting capital：$21.7M (Dec 2022); $18.6M DARPA OPTIMA grant (2024); >$100M Series B led by Tiger Global (Feb 2025) - total ~$144M.
How it started
Verma's Princeton lab researched charge-based analog in-memory computing for about a decade with Department of Defense funding; he spun out EnCharge AI in 2022 with ex-IBM CTO Kailash Gopalakrishnan and ex-Macom COO Echere Iroaga, raising $21.7M to build a complete hardware-plus-software stack.
What happened
Feb 2025: oversubscribed >$100M Series B led by Tiger Global, with Samsung Ventures, Foxconn-linked HH-CTBC, RTX Ventures, and In-Q-Tel; first product EN100 (M.2 module: 200+ TOPS at 8.25W, 32GB LPDDR; 4-chip PCIe card: ~1 petaOPS at 40W) unveiled, with trade-press coverage into 2026; targeting laptops, workstations, robotics, and industrial automation.
How it ended up
EN100 launched and in commercialization; the company remains private, with no revenue or customer names disclosed.
Background
EnCharge AI is a Santa Clara semiconductor startup betting that the future of AI compute is analog, not digital. Its chips perform multiply-accumulate operations inside memory by reading electrical charge on metal capacitors, eliminating the data shuttling between memory and processor that dominates power use. The company claims roughly 20x better performance-per-watt than conventional accelerators.
The technology came out of Princeton professor Naveen Verma's lab, which worked on charge-based analog in-memory computing for about a decade with Department of Defense funding before the company spun out in 2022 with CTO Kailash Gopalakrishnan (ex-IBM) and COO Echere Iroaga (ex-Macom). EnCharge raised $21.7M in December 2022, then an oversubscribed >$100M Series B led by Tiger Global in February 2025, bringing total funding to roughly $144M; DARPA added an $18.6M OPTIMA grant in 2024.
Its first product, the EN100, delivers 200+ TOPS inside an 8.25W envelope on an M.2 laptop module with 32GB LPDDR, and about 1 petaOPS on a 40W four-chip PCIe card for workstations, positioning local inference as an alternative to cloud GPUs. Investors include Samsung Ventures, Foxconn-linked HH-CTBC, RTX Ventures, and In-Q-Tel. As of April 2026 the company is commercializing EN100, with launch coverage in EDN Japan, Parola Analytics, and EE Journal.
What has to be true
- Analog in-memory computing attacks the biggest cost in AI inference, moving data between memory and compute, rather than just scaling digital chips.
- A decade of Princeton research and DoD funding de-risked the technology before venture money changed the company's agenda.
- The investor list spanning Samsung, Foxconn, RTX, and In-Q-Tel signals demand for efficient on-device AI across consumer electronics, manufacturing, and defense.
- The bet is contrarian: if efficiency wins, edge devices displace cloud GPUs for everyday inference; if digital roadmaps keep improving, the window closes.
What can be applied
De-risking fundamental tech for a decade before venture money gave EnCharge patents and a contrarian story, but analog inference still must win OEM sockets against Nvidia's roadmap.
Aftermath
As of April 2026, EnCharge has launched its first product, EN100, and is pursuing OEM adoption in laptops, workstations, and edge/industrial systems; it remains a private company of roughly 90 people in Santa Clara. No revenue or named customers have been disclosed, and the claim that analog inference will displace digital accelerators is still unproven at volume.
Sources
- EnCharge raises $100M+ to accelerate AI using analog chips
- AI chip startup EnCharge AI raises $21.7M to build integrated stack for edge uses
- エッジAI用半導体 10選
- Patent Snapshot: EnCharge AI and its 'first-of-its-kind' on-device AI accelerator
- EnCharge Me Up! 200 AI TOPS at Only ~8 Watts
- EnCharge AI - company dossier
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