The AI arms race spawns new hardware architectures
As society turns to artificial intelligence to solve problems across ever more domains, we’re seeing an arms race to create specialized hardware that can run deep learning models at higher speeds and lower power consumption.
Some recent breakthroughs in this race include new chip architectures that perform computations in ways that are fundamentally different from what we’ve seen before. Looking at their capabilities gives us an idea of the kinds of AI applications we could see emerging over the next couple of years.
Neural networks, composed of thousands and millions of small programs that perform simple calculations to perform complicated tasks such as detecting objects in images or converting speech to text are key to deep learning.
But traditional computers are not optimized for neural network operations. Instead they are composed of one or several powerful central processing units (CPU). Neuromorphic computers use an alternative chip architecture to physically represent neural networks. Neuromorphic chips are composed of many physical artificial neurons that directly correspond to their software counterparts. This make them especially fast at training and running neural networks.
The concept behind neuromorphic computing has existed since the 1980s, but it did not get much attention because neural networks were mostly dismissed as too inefficient. With renewed interest in deep learning and neural networks in the past few years, research on neuromorphic chips has also received new attention.
In July, a group of Chinese researchers introduced Tianjic, a single neuromorphic chip that could solve a multitude of problems, including object detection, navigation, and voice recognition. The researchers showed the chip’s functionality by incorporating it into a self-driving bicycle that responded to voice commands. “Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” the researchers observed in a paper published in Nature.
While there’s no direct evidence that neuromorphic chips are the right path to creating artificial general intelligence, they will certainly help create more efficient AI hardware.
Neuromorphic computing has also drawn the attention of large tech companies. Earlier this year, Intel introduced Pohoiki Beach, a computer packed with 64 Intel Loihi neuromorphic chips, capable of simulating a total of 8 million artificial neurons. Loihi processes information up to 1,000 times faster and 10,000 more efficiently than traditional processors, according to Intel.
Neural networks and deep learning computations require huge amounts of compute resources and electricity. The carbon footprint of AI has become an environmental concern. The energy consumption of neural nets also limits their deployment in environments where there’s limited power, such as battery-powered devices.
