EnCharge AI, a semiconductor startup developing analog memory chips for AI applications, has raised more than $100 million in a Series B round led by Tiger Global to spur its next stage of growth. 

The funding is significant partly because interest in AI is at an all-time high, but the high price of building and operating AI services continues to be a red flag. EnCharge, spun out from Princeton University, believes its analog memory chips — envisioned to be embedded in devices such as laptops, desktops, handsets and wearables — will not only speed up AI processing, they’ll help bring the cost down as well. 

Santa Clara-based EnCharge claims its AI accelerators use 20 times less energy to run workloads compared with other chips on the market, and expects to have the first of those chips on the market later this year.

EnCharge’s fundraise is notable because it comes at a time when the U.S. government has identified hardware and infrastructure (including chips) as two key areas where it wants to boost domestic innovation and products. If it’s successful in its execution, EnCharge could become a key part of that strategy. 

This Series B is a fresh round of funding, the company has confirmed to me. Of note: a tranche of funding we reported in December 2023, was not part of this Series B. There was a hint of this Series B last May, when Bloomberg reported that EnCharge wanted to raise at least $70 million more to expand its business.

In an interview with TechCrunch, EnCharge’s CEO and co-founder Naveen Verma would not disclose the company’s valuation. PitchBook data that indicates EnCharge raised money in October at a $438 million post-money valuation is incorrect, the company told TechCrunch.

Verma also would not disclose who its customers are, but the funding is coming from an interesting and long list of strategic and financial investors that indicate who is likely working with the startup. 

In addition to Tiger Global, others in the round include Maverick Silicon, Capital TEN (from Taiwan), SIP Global Partners, Zero Infinity Partners, CTBC VC, Vanderbilt University and Morgan Creek Digital, along returning investors RTX Ventures, Anzu Partners, Scout Ventures, AlleyCorp, ACVC and S5V. 

Corporations that invested in the round include Samsung Ventures and HH-CTBC — a partnership between Hon Hai Technology Group (Foxconn) and CTBC VC. Previously, the VentureTech Alliance also backed EnCharge. Others include In-Q-Tel (the government-backed investor associated with the CIA), RTX Ventures (the VC arm of the aerospace and defense contractor), and Constellation Technology (a clean energy manufacturer). The startup has also received grants from U.S. organizations like DARPA and the Department of Defense. 

Verma said EnCharge is working closely with TSMC. He previously said TSMC would be the company manufacturing its first chips. 

“TSMC has been following my research for many, many years,” he said in an interview, adding that the involvement dated back to the early stages of EnCharge’s R&D. “They’ve given us access to very advanced silicon. That’s a very rare thing for them to do.”

Analog focus

With its focus on analog, EnCharge is taking a different approach than its competitors. So far, all eyes have been focused on the processing chips used for training and AI inference at the server end, which has translated into a major surge of business for GPU makers like Nvidia and AMD. 

The difference with EnCharge’s approach is laid out in a recent paper on analog chips from IBM’s research team. As IBM’s researchers explain it, there is “no separation between compute and memory, making these processors exceptionally economical compared to traditional designs.” 

IBM, like EnCharge, also comes to the conclusion that so far, the physical properties of these chips makes them OK for inference, but less good for training. EnCharge chips are not used for training applications, but to run existing AI models at “the edge.” But the startup (and others, like IBM) continue to work on new algorithms that could expand the use cases.

IBM and EnCharge are not the only companies working on analog approaches. But as Verma explains it, one of EnCharge’s breakthroughs has been in the design of its chips, specifically making them noise-resilient. 

“If you have 100 billion transistors on a chip, they can all have noise, and you need them all to work, so you want to have that signal separation. But you’re also leaving a lot of efficiency on the table because you’re not representing all these signals in between analog attempts to do that,” Verma explained. “The big breakthrough we had is figuring out how to make analog not sensitive to noise.”

The company uses “a very precise device that you get for free in standard supply chain,” he said, explaining that device is a set of geometry-dependent metal wires that “you can control them very, very well.” 

The company, Verma says, is full-stack: It has also developed software around its hardware. 

It helps EnCharge’s case that Verma and his co-founders, COO Echere Iroaga and CTO Kailash Gopalakrishnan (left and right above, with Verma center) — who respectively previously worked at semiconductor company Macom and IBM — bring a lot of expertise to the table. But it remains to be seen whether this will be enough to keep EnCharge competitive in an extremely crowded market. Other startups in the analog chip race include Mythic and Sagence.

“We at Anzu have looked at probably 50-plus companies in this space — at least 50 between 2017 and 2021, and probably more than 50 since then,” said Jimmy Kan, an investment partner focused on semiconductors for Anzu Partners, who previously worked on chips at Qualcomm.

“One out of every five of those was some sort of new novel architecture like analog or spiking neural network computation chips. We really had it in our mind to find an AI compute technology that was really, really differentiated, versus incremental, versus something that Nvidia might just develop next quarter or next year,” he added. “So we’re really, really excited to see the progress that EnCharge has made.”

EnCharge’s rise is in contrast to how a lot of deep tech startups have developed over the last several years. 

One knock-on effect of the technology boom of the last 25 years has been the ample venture funding ready to back startups building what could be the next Google, Microsoft, Apple, Meta or Amazon. That, in turn, has spilled into a much bigger pool of startups in the market.

That pool has seen an increasing number of deep tech efforts: Smart founders raising money not for finished products, but interesting ideas that are not yet market-ready but could be a big deal if they are brought into the world. Quantum computing is a classic “deep tech” category, for example.

EnCharge could have easily been one of that wave of deep tech businesses, if it had spun out earlier from Princeton and worked quietly with venture and other funding to possibly build the next innovation in chips.

But the startup waited years to venture out on its own. It was in 2022, nearly a decade after Verma and his team first started their research at Princeton, that the company emerged from stealth and started work on securing commercial partners while continuing to develop its technology. 

“There’s certain kinds of innovations where you can jump to venture backing very early on. But if what you’re doing is developing a fundamentally new technology, there’s a lot of aspects of that that need to be understood to de-risk that a lot of them fail,” Verma said. “The day you take venture funding, your agenda changes… It’s no longer about understanding the technology. You have to be customer-focused.”