According to reports, startup EnCharge AI raised more than $100 million in a Series B funding round led by Tiger Global to bring more efficient and less expensive AI chips to the market.

EnCharge AI creates analog chips that are incorporated into storage semiconductors. These in-memory chips are made for inference, which is a stage in which AI models are used instead of being taught.

The majority of AI inference chips are found in large server clusters in data centers, however, EnCharge AI's chips are made for edge computing and are used in laptops and other user-facing devices.

Their approach to embedding analog processing in memory chips allows their accelerators to perform AI tasks with up to 20 times less energy consumption compared to some of the leading AI chips, as per reports.

“Battery-powered devices like laptops and smartphones need efficient chips to process AI and analog chips situated inside semiconductors for memory are a viable solution,” says CEO Naveen Verma.

"It turns out that these platforms can now really overcome many of the barriers in terms of cost and sustainability, but also in terms of privacy and security, which the enterprise and also a lot of consumer applications care very much about," adds Naveen.

Groq, founded by a former Alphabet chip engineer and Cerebras, is among the companies developing specialized chips for AI inference.

Other investors in the round included Samsung Electronics' VC arm and HH-CTBC, a partnership between Taiwan's Foxconn and CTBC Venture Capital.