Something to think about. Forbes has produced a series of articles on the issues raised by the rise of Generative Artificial Intelligence and I have extracted the following paragraphs from just two of these articles. I would encourage you to read the full articles by taking advantage of the links provided:
“The rapid progress of Generative Artificial Intelligence (GenAI) has raised concerns about the sustainable economics of emerging GenAI services. Can Microsoft, Google, and Baidu offer chat responses to every search query made by billions of global smartphone and PC users? One possible resolution to this challenge is to perform a significant proportion of GenAI processing on edge devices, such as personal computers, tablets, smartphones, extended reality (XR) headsets, and eventually wearable devices”
“The Impact of On-Device GenAI on Forecasted TCO
According to the Tirias Research GenAI Forecast and TCO Model, if 20% of GenAI processing workload could be offloaded from data centers by 2028 using on-device and hybrid processing, then the cost of data center infrastructure and operating cost for GenAI processing would decline by $15 billion. This also reduces the overall data center power requirements for GenAI applications by 800 megawatts. When factoring in the efficiencies of various forms of power generation, this results in a savings of approximately 2.4 million metric tons of coal, the reduction of 93 GE Halide 14MW wind turbines, or the elimination of several million solar panels plus and associated power storage capacity. Moving these models to devices or hybrid also reduces latency while increasing data privacy and security for a better user experience, factors that have been promoted for many consumer applications, not just AI.”
“Event-driven AI
As an alternative, BrainChip has developed its Akida neuromorphic IP (intellectual property) solutions to support Temporal Event-based Neural Networks (TENNs), an event-based neural processing network architecture, in addition to traditional Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs) and Transformer based neural networks. What this means is that a temporal (time) enabled neural network (TENN), or networks, is only operating during the time when a trigger event or input occurs. During other times, it is not computing and therefore not consuming much power. This can translate to higher performance, adaptability and lower real-time latency per event, input, or request and at a fraction of the power consumption of other AI solutions.
According to BrainChip, TENNs are ideal for processing various types of data such as one-dimensional time series and spatial-temporal data. TENNs is showing positive results in a number common applications, such as audio denoising, eye-tracking for AR/VR, health data monitoring (heart rate, SpO2), keyword spotting, Small Language Models (SLMs), and video object detection. TENNs ability to adapt to new input/events overcome some of the limitations of traditional neural networks while supporting future neural network models at a fraction of the die area and operating cost.”
“According to one of the company’s demonstrations, using TENNs for object detection can provide a 5x to 30x reduction in operations using 1/50th the number of parameters with equal or better accuracy than traditional CNNs. The real value, however, is that it can be accomplished in less than 20mW, meaning that by integrating the Akida technology into or with a low-power microcontroller it can provide real-time processing in a battery-powered application.”
“In another demonstration, the company demonstrated how TENNs can be used to drastically reduce the training time by and the power consumed by more than orders of magnitude relative to other large language data sets like GPT-2, which would be more appropriate for embedded applications than the newer models, with equivalent accuracy.”
“Edge Specific Solutions
While there is a rush to push AI to every platform and device, scaling down from the data center may not be the best solution for many applications. As we have seen in the past, the unique requirements of devices often drive innovation in new directions, Tirias Research believes the same will hold true for AI as it moves from the datacenter to the edge. But, as with any new technology, success often depends on the benefit over existing solutions. According to BrainChip, the numbers can be very significant, with demonstrations showing up to a 50x reduction in the number of model parameters, up to a 30x reduction in training time, and 5000x reduction in multiple-accumulate (MAC) operations with the same or better accuracy. Improvements in performance and power efficiency scale with model efficiency.”
It seems inevitable with performance gains of this order and financial savings in the multi billions of dollars someone significant will bite the bullet and adopt the only pure play listed Brainchip Inc's AKIDA TENNS and shift GENAi to the Edge wherever and whenever possible.
There are only so many mothballed nuclear power plants available for big tech to reopen for a minimum of $US1.5 billion as Microsoft is currently doing to keep the Generative Ai lights on.
As every western country races to the bottom where the preservation of base load power is concerned the pressure to adopt any and all technology that can do what AKIDA TENNS is capable of will become overwhelming.
My opinion only DYOR
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