Cheap Computing and the Balancing
Act of Population Decline

To counteract these challenges, the focus must shift towards increasing productivity without human overload. In other words, we need to get more human equivalent. Historically, automation has played a key role in boosting productivity. However, the recent decrease in computational costs suggests a potential profound transformation on the horizon. The promise of almost free computer power has significant implications for productivity growth.

The ESP32-CAM from Espressif, a low-cost development kit capable of computer vision applications, perfectly exemplifies the advent of cheap computing. In fact, in an article where it was presented 5 years ago it prompted the author to say computing [with ESP32] is not just cheap but it’s essentially free [5]. This seemingly trivial device’s potential is immense, setting a precedent for affordable compute power that creates value from virtually nothing.

When I learned about Espressif, I was working at Intel in the technology department building the MyriadX chip. I tried out ESP32 in my spare time because it had some similarities to Myriad: it could use a camera, do some level of image processing, and claimed to do computer vision applications. A couple of weekends later, I was able to build a few interesting applications and was very excited. But then I looked at machine learning applications on ESP32, and the performance there was really limiting my ideas. Even so, the article comment was something that stuck in my mind: a platform that makes compute essentially free.

This revelation came back into focus when during an internal brainstorming session, Fabrizio del Maffeo, our CEO at Axelera, commented that we were essentially getting Tera Operations Per Second (TOPS) for free with In-Memory Computing. At the time the comment was made, we were still waiting for Metis, Axelera’s first commercially available chip to come from the factory into the lab. The comment still felt theoretical.

Things changed just a few days later when Metis came into the lab. Incidentally, Metis’ early power-up coincided with the week before and after Easter. In Romania, where I am based, in a word-by-word translation these weeks would be “The Great Week” and “The Enlightened Week” which seemed fitting: Metis came alive in the Great Week and started running Neural Networks in the Enlightened Week. To introduce Metis to the world we decided to use the MIT Lincoln Laboratory [6] report which charted current compute trends with a live demonstration.

Metis AIPU was not there yet at the time of publishing the 2022 trends but we did a live plot running internal max TOPS programs for the In-Memory Computing engines.

This chart doesn’t show the compute cost but all this comes at around $200. Now, the human brain memory power is likened to around 100 trillion calculations [also called „operations”] per second [7]. The program used above was an artificial one loading the In-Memory Computing part of Metis with an artifical neural net-like that had some load/store overheads to an ideal pure-calculation program measuring the AIPU In-Memory Computing power used for this. It reaches a practical 200 TOPS result, so the equivalent of two human brains. There is a lot more work to reach actual use of human brains but what really shook me was that price for such brain power. Such calculation capacity has existed for quite sometime now. Still, the computing platforms needed for it have had wild price evolutions so far and have generally stayed in the thousands of dollars. But $100 for a human brain capacity is eye opening and has significant transformation potential. Currently, the smallest country nominal GDP per capita in the world is $249. With the capacity of two human brains lowered at less than half of that, we are witnessing the first point in history where the compute equivalent of human productivity is clearly available anywhere in the world, creating the ability to democratize AI to even the poorest of countries.

To look at the practical implications of this capacity we can look at analysis recently made showing the potential to double the productivity in China which is one of the countries already facing some of the most serious population declines and hedging their hopes on automation [8]. Doubling productivity could more than compensate for an eventual productivity decline caused by population shrinkage.

The rise of affordable computation, coupled with advances in AI, could provide tangible relief to pension funds. By boosting overall productivity, the returns from investment-based pension funds could improve, and profit tax revenues for state-financed pension funds could increase. This shift could effectively stabilize pension fund sustainability even with a smaller proportion of working individuals. It could also potentially improve living standards for retirees, without an increased financial burden on the younger generation.

Additionally, an increased reliance on automation could also alleviate workload stress for workers. By automating repetitive tasks and processes, workers could focus on more engaging, creative aspects of their jobs. A reduction in workload stress could lead to improved job satisfaction and mental well-being, contributing to an overall higher quality of life.