Semiconductor startup EnCharge AI has raised over $100 million in a Series B funding round led by Tiger Global, a significant investment that will propel the company's next stage of growth. This funding comes at a time when interest in AI is at an all-time high, but the high cost of building and operating AI services remains a major concern.

EnCharge, spun out from Princeton University, is developing analog memory chips designed to be embedded in devices such as laptops, desktops, handsets, and wearables. These chips aim to not only speed up AI processing but also bring down the cost. The company claims its AI accelerators use 20 times less energy to run workloads compared to other chips on the market, with the first of these chips expected to hit the market later this year.

The funding is notable, especially considering the U.S. government's focus on boosting domestic innovation and products in hardware and infrastructure, including chips. If EnCharge is successful in its execution, it could become a key player in this strategy.

This Series B funding is a fresh round, separate from a tranche of funding reported in December 2023. EnCharge's CEO and co-founder, Naveen Verma, would not disclose the company's valuation, but PitchBook data indicating a $438 million post-money valuation is incorrect, according to the company.

Verma also declined to reveal EnCharge's customers, but the list of strategic and financial investors provides insight into who is likely working with the startup. Investors include Maverick Silicon, Capital TEN, SIP Global Partners, Zero Infinity Partners, CTBC VC, Vanderbilt University, and Morgan Creek Digital, along with 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. EnCharge has also received grants from U.S. organizations like DARPA and the Department of Defense. The startup is working closely with TSMC, which will manufacture its first chips.

EnCharge's focus on analog memory chips sets it apart from competitors, who have primarily focused on processing chips used for training and AI inference at the server end. This has led to a surge in business for GPU makers like Nvidia and AMD. EnCharge's approach, as explained in a recent paper by IBM's research team, eliminates the separation between compute and memory, making its processors exceptionally economical compared to traditional designs.

IBM, like EnCharge, has concluded that analog chips are suitable for inference but less effective for training. EnCharge's chips are designed to run existing AI models at "the edge," but the startup is working on new algorithms to expand its use cases. EnCharge's breakthrough lies in making its analog chips noise-resilient, a critical aspect of its design.

The company's full-stack approach, which includes software development around its hardware, is also noteworthy. EnCharge's founders, who bring extensive expertise from semiconductor companies like Macom and IBM, have developed a unique solution that could disrupt the AI processing landscape.

While EnCharge's rise is promising, it remains to be seen whether the company can maintain its competitive edge in an extremely crowded market. Other startups, such as Mythic and Sagence, are also working on analog chip solutions. However, EnCharge's differentiated approach and significant funding provide a strong foundation for its future growth.

The startup's journey is also a testament to the importance of patience and careful development in the deep tech space. EnCharge waited years to venture out on its own, focusing on understanding its technology before seeking commercial partners and venture funding. This approach has allowed the company to de-risk its innovation and prioritize customer needs.

As the AI landscape continues to evolve, EnCharge AI's innovative approach and significant funding position it as a key player in the development of more efficient and cost-effective AI processing solutions.