What's old is new again. At least, it is if we are talking about analog computing. The moment you hear the phrase "analog computing," you might be forgiven for thinking we are talking about the hipsters of the technology world. The people who prefer vinyl over Spotify. The ones that want to bring back typewriters to replace word processors, or the folks who prize handwritten notes over those generated by ChatGPT.

Irvine Calif.-based Syntiant thinks it can use embedded flash memory to greatly reduce the amount of power needed to perform deep-learning computations. Austin, Tex.-based Mythic thinks it can use embedded flash memory to greatly reduce the amount of power needed to perform deep-learning computations. They both might be right. A growing crowd of companies is hoping to deliver chips that accelerate otherwise onerous deep learning applications, and to some degree they all have similarities because "these are solutions that are created by the shape of the problem," explains Mythic founder and CTO Dave Fick. When executed in a CPU, that problem is shaped like a traffic jam of data. A neural network is made up of connections and "weights" that denote how strong those connections are, and having to move those weights around so they can be represented digitally in the right place and time is the major energy expenditure in doing deep learning today.

Before it started scampering after the machine learning chip market in 2016, but after it was founded at the University of Michigan in 2012, Mythic was trying to build embedded chips that would let surveillance drones run software modeled after the human brain. Part of the funding for the company, then known as Isocline, came from the Department of Defense. But after relaunching two years ago, Mythic refocused on embedded devices like autonomous cars and security cameras. Now the company is only a few months from sampling chips based on an aggressively ambitious architecture, which uses analog computing inside flash memory cells to accelerate machine learning tasks like facial recognition. Helping it over the finish line is $40 million raised last month from new and existing investors, including SoftBank Ventures, Draper Fisher Jurvetson and Lux Capital.

Before it started scampering after the machine learning chip market in 2016, but after it was founded at the University of Michigan in 2012, Mythic was trying to build embedded chips that would let surveillance drones run software modeled after the human brain. Part of the funding for the company, then known as Isocline, came from the Department of Defense. But after relaunching two years ago, Mythic refocused on embedded devices like autonomous cars and security cameras. Now the company is only a few months from sampling chips based on an aggressively ambitious architecture, which uses analog computing inside flash memory cells to accelerate machine learning tasks like facial recognition. Helping it over the finish line is $40 million raised last month from new and existing investors, including SoftBank Ventures, Draper Fisher Jurvetson and Lux Capital.