The researchers’ compiler takes as input differential equations, which biologists frequently use to describe cell dynamics, and translates them into voltages and current flows across an analog chip. “And it seemed like a very exciting new platform.” “I happened to run into Rahul at a party, and he told me about this platform he had,” Rinard says. Sarpeshkar, a former MIT professor and currently a visiting scientist at the Research Lab of Electronics, has long studied the use of analog circuits to simulate cells. Kurtz Professor and professor of engineering, physics, and microbiology and immunology at Dartmouth. They’re joined by Rahul Sarpeshkar, the Thomas E. The first author on the paper is Sara Achour, a graduate student in electrical engineering and computer science, advised by Rinard. I want to go off and fundamentally change things and see where I can get.” “The digital hardware platform has been very heavily optimized for the current set of applications. “At some point, I just got tired of the old digital hardware platform,” says Martin Rinard, an MIT professor of electrical engineering and computer science and a co-author on the paper describing the new compiler. The work could help pave the way to highly efficient, highly accurate analog simulations of entire organs, if not organisms. Last week, at the Association for Computing Machinery’s conference on Programming Language Design and Implementation, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory and Dartmouth College presented a new compiler for analog computers, a program that translates between high-level instructions written in a language intelligible to humans and the low-level specifications of circuit connections in an analog computer. But existing analog computers have to be programmed by hand, a complex process that would be prohibitively time consuming for large-scale simulations.
In recent years, analog computers have proven to be much more efficient at simulating biological systems than digital computers. Digital computing, for all its advantages, leaves most of transistors’ informational capacity on the table.
A transistor, conceived of in digital terms, has two states: on and off, which can represent the 1s and 0s of binary arithmetic.īut in analog terms, the transistor has an infinite number of states, which could, in principle, represent an infinite range of mathematical values.
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