Graphics processing units (GPUs), the chips on which most AI models run, are energy-hungry beasts. As a consequence of the accelerating incorporation of GPUs in data centers, AI will drive a 160% uptick in electricity demand by 2030, Goldman Sachs estimates.
The trend isn’t sustainable, argues Vishal Sarin, an analog and memory circuit designer. After working in the chip industry for over a decade, Sarin launched Sagence AI (it previously went by the name Analog Inference) to design energy-efficient alternatives to GPUs.
“The applications that could make practical AI computing truly pervasive are restricted because the devices and systems processing the data cannot achieve the required performance,” Sarin said. “Our mission is to break the performance and economics limitations, and in an environmentally responsible way.”
Sagence develops chips and systems for running AI models, as well as the software to program these chips. While there’s no shortage of companies creating custom AI hardware, Sagence is somewhat unique in that its chips are analog, not digital.
Most chips, including GPUs, store information digitally, as binary strings of ones and zeros. In contrast, analog chips can represent data using a range of different values.
Analog chips aren’t a new concept. They had their heyday from about 1935 to 1980, helping model the North American electrical grid, among other engineering feats. But the drawbacks of digital chips are making analog attractive once again.
For one, digital chips require hundreds of components to perform certain calculations that analog chips can achieve with just a few modules. Digital chips also usually have to shuttle data back and forth from memory to processors, causing bottlenecks.
“All the leading legacy suppliers of AI silicon use this old architectural approach, and this is blocking the progress of AI adoption,” Sarin said.
Analog chips like Sagence’s, which are “in-memory” chips, don’t transfer data from memory to processors, potentially enabling them to complete tasks faster. And, thanks to their ability to use a range of values to store data, analog chips can have higher data-density than their digital counterparts.
Analog tech has its downsides, however. For example, it can be harder to achieve high precision with analog chips because they require more accurate manufacturing. They also tend to be tougher to program.
But Sarin sees Sagence’s chips complementing — not replacing — digital chips, for example, to accelerate specialized applications in servers and mobile devices.
“Sagence products are designed to eliminate the power, cost and latency issues inherent in GPU hardware, while delivering high performance for AI applications,” he said.
Sagence, which plans to bring its chips to market in 2025, is engaged with “multiple” customers as it looks to compete with other AI analog chip ventures like EnCharge and Mythic, Sarin said. “We’re currently packaging our core technology into system-level products and ensuring that we fit into existing infrastructure and deployment scenarios,” he added.
Sagence has secured investments from backers including Vinod Khosla, TDK Ventures, Cambium Capital, Blue Ivy Ventures, Aramco Ventures and New Science Ventures, raising a total of $58 million in the six years since its founding.
Now, the startup is planning to raise capital again to expand its 75-person team.
“Our cost structure is favorable because we’re not chasing the performance goals by migrating to the newest [manufacturing processes] for our chips,” Sarin said. “That’s a big factor for us.”
The timing might just work in Sagence’s favor. Per Crunchbase, funding to semiconductor startups appears to be bouncing back after a lackluster 2023. From January to July, VC-backed chip startups raised nearly $5.3 billion — a number well ahead of last year, when such firms saw less than $8.8 billion raised in total.
This being the case, chipmaking is an expensive proposition — made all the more challenging by international sanctions and tariffs promised by the incoming Trump administration. Winning customers who’ve become “locked in” to ecosystems like Nvidia’s is another uphill climb. Last year, AI chipmaker Graphcore, which raised nearly $700 million and was once valued at close to $3 billion, filed for insolvency after struggling to gain a strong foothold in the market.
To have any chance at success, Sagence will have to prove that its chips do, indeed, draw dramatically less power and deliver higher efficiency than alternatives — and raise enough venture funding to fabricate at scale.