It is widely accepted that the US is unsurprisingly leading the way in Artificial Intelligence with China a distant second and the rest of the world nowhere in sight. At the same time, Indian technology has been perceived to at best digitise offline businesses through fintech, edtech, healthtech, etc as opposed to delivering any genuinely deep technology breakthroughs. Both these seem set to change according to news coming from India’s premier science and technology institution – The Indian Institute of Science (IISc) earlier this week. For the geeks among you, the research paper can be accessed here. For the rest of us laymen, this piece in the Deccan Herald tries to explain it in somewhat simple English.
From the sounds of it, the breakthrough has the potential to put to waste the tens of billions of dollars invested by hyperscalers (Amazon, Microsoft, Google, Meta) on GPU computing data centres for Generative AI. That sounds like a big deal. So what exactly is this breakthrough?
“What they have done essentially is to develop a type of semiconductor device called Memristor, but using a metal-organic film rather than conventional silicon-based technology. This material enables the Memristor to mimic the way the biological brain processes information using networks of neurons and synapses, rather than do it the way digital computers do.
…The Memristor, when integrated with a conventional digital computer, enhances its energy and speed performance by hundreds of times, thus becoming an extremely energy-efficient ‘AI accelerator’.…When eventually scaled up, the technology could enable the most large-scale and complex AI tasks – such as large language model (LLM) training – to be done on a laptop or smartphone, rather than requiring a data centre.”
What’s wrong with processing AI tasks at a data centre?
“… the problem arises due to the fact that the processor and memory units are separated by a distance and data needs to constantly go back and forth between them for it to be processed, limiting computational speed and adding a hefty energy penalty.…As a result, if you have to build AI systems, which require feeding and processing enormous amounts of data into computers, they can only be done in the ‘cloud’, which is basically a lovely but misleading term for large data centres, which need massive amounts of energy to keep the computing infrastructure cool. It is estimated that if computing continues on this path, by 2050, the power needed to run AI data centres will have outstripped the world’s power generation capacity.”
How does the IISc team’s breakthrough solve this problem?
“…team took the metal-organic film approach to Memristors, which too is an In-Memory Computing approach but which actually works like the brain’s neuron-synapse circuit. “Through the 2010s, large companies tried to mimic the brain while sticking to silicon transistors. They realised that they were not making significant gains. In the 2020s, the research investments are moving back to academia because there is a realisation that we need much more fundamental discoveries to actually achieve brain-inspired computing. If you just take a brute-force approach to use transistors and enforce certain algorithms, that’s not going to work,” Prof Sreetosh Goswami told DH.
Our analog brain operates differently from a digital computer. Memory and processing are not discreet in the brain. Also, unlike digital computers, it does not process information as 0s and 1s, and it does not rely on breaking the proverbial elephant to be eaten into small pieces. Instead, it processes information by swallowing big chunks of data, reducing the number of steps required to get to the answer drastically. The brain is therefore extremely energy-efficient. The combination of analog computing and brain-mimicking Memristor technology makes neuromorphic computing fast and highly efficient.”
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