The cover image shows the Loihi 2 chip on a small/ pinky finger. Of course, the chip is getting a huge jump in new resources, and the way it is doing so is not by getting bigger. Intel Labs realized that actually carrying magnitude could help convey more information and be beneficial relative to the cost of adding magnitude as s dimension, so this is an example where nature is informing, and humans are evolving based on math. One we will highlight is that in nature neurons generally fire in binary spikes. Those bigger changes are certainly implemented.
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One needs to keep in mind that this is a relatively newer area for chip design where the software frameworks are changing so we can expect big changes from the lessons learned. Here are the key lessons learned from Loihi. Since today’s announcement is Loihi 2, the “2” means this is a second-generation chip. Conventional Parallel And Neuromorphic Computing Something being called out here is the lack of off-chip memory. Here is a quick comparison basically with CPU, parallel (GPU), and neuromorphic compute. Intel Loihi 2 Intelligence V Deep Learning Instead, the goal is to quickly and efficiently learn and apply what is learned. So the goal of neuromorphic computing is not necessarily to get the correct answer 100% of the time. Traditional deep learning models are being deployed today but the cost and power consumption are increasing at a faster rate.
Intel is undertaking this research because brains are able to train on very minimal data sets and relatively quickly. Compared to a drone, the parrot has some unique capabilities (although it cannot, for example, organically record video and stream that video), and at much lower power consumption. The parrot has a small lightweight and low-power brain yet is autonomous and can learn new tasks and adapt to environments in real time. People often refer to making human brain like machines, but Intel here is using a parrot. Normally we skip these examples, but this one hit home. Intel Loihi 2 Neuromorphic Compute Tile on Intel 4 The basic idea is to inform a compute platform on neuroscience with how brains work and use that framework to complete tasks in a more energy-efficient manner. This is a different approach, although perhaps not quite as dramatic as quantum computing, but it is also a major shift. Intel has been researching a newer area of compute called Neuromorphic computing.