If transistors can’t get smaller, then coders have to get smarter
In 1965, Intel co-founder Gordon Moore predicted that the number of transistors that could fit on a computer chip would grow exponentially — and they did, doubling about every two years. For half a century, Moore’s Law has endured: Computers have gotten smaller, faster, cheaper, and more efficient, enabling the rapid worldwide adoption of PCs, smartphones, high-speed internet, and more.
This miniaturization trend has led to silicon chips today that have almost unimaginably small circuitry. Transistors, the tiny switches that implement computer microprocessors, are so small that 1,000 of them laid end-to-end are no wider than a human hair. And for a long time, the smaller the transistors were, the faster they could switch. But today, we’re approaching the limit of how small transistors can get. As a result, over the past decade researchers have been scratching their heads to find other ways to improve performance so that the computer industry can continue to innovate.
While we wait for the maturation of new computing technologies like quantum, carbon nanotubes, or photonics (which may take a while), other approaches will be needed to get performance as Moore’s Law comes to an end. In a recent journal article published in Science, a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) identifies three key areas to prioritize to continue to deliver computing speed-ups: better software, new algorithms, and more streamlined hardware.
Senior author Charles E. Leiserson says that the performance benefits from miniaturization have been so great that, for decades, programmers have been able to prioritize making code-writing easier rather than making the code itself run faster. The inefficiency that this tendency introduces has been acceptable, because faster computer chips have always been able to pick up the slack.