What are Neural Class Networks?

In 1971 Intel released the 4004. The 4004 was their first 4-bit single core processor, and for the next 34 years, that’s pretty much how x86 computing progressed. Sure they bumped up the architecture and speed as designs and processes improved, but one thing remained constant a single processing engine. Under pressure in the 1990s from Unix workstations driven by Reduced Instruction Set Computing (RISC) with multi-core pipeline architectures like IBM’s PowerPC, Sun’s Ultra-SPARC and the MIPS R3000 Intel began exploring multi-core architectures.

So from 1971 until 2005, every x86 processor had a single core, life was simple. Intel even provided reference designs so system builders could even put two of these processors into the same system. Sure there were fringe companies that developed Symmetrical Multi-Processing (SMP) systems (ex. Sequent & NEC) with more than two CPU sockets, but they were large frame expensive custom servers not found in general mainstream use. So if you wanted to scale out your computational capacity to tackle a tough problem, you had to rack more servers.

This single core challenge largely drove commodity Linux clustering making it all the rage by the turn of the century, particularly in high-performance computing (HPC). It wasn’t uncommon to tightly couple 1,000 or more dual socket single core systems together to tackle a tough computational problem. Government agencies leveraged large clusters to model our nuclear stockpile and computationally secure it. Auto companies crashed dozens of virtual car designs on a daily basis, and oil companies crunched seismic data to compute untapped oil reserves. Then the game changed, and x86 shifted to multi-core processors. Yesterday Intel announced the availability of their new Skylake server platform, but the industry is already refocusing on 2018’s Cascade Lake 32 core, 64 thread, server chip. So why does all this matter?

As a general rule, Google doesn’t publish or confirm the computation capacity of their data centers. If we pick a specific example, their Oregon data center, we can apply particular assumptions and project that it’s roughly 100,000 servers. Again assuming they are using dual socket eight core hyper-threaded processors this translates to 3.2 million parallel threads of computation tightly coupled in one physical location potentially addressing a single problem. Structures like this are quickly approximating what took nature millions of years to develop, an organic brain based on the neuron. Neurons on average have 7,000 connections to both local and remote neurons within their system, that’s a considerable amount of networking per single computational unit.

By contrast, the common cockroach has one million neurons, and that frog you played with as a kid sports 16 million. So if we equate a neuron to a single thread of execution on an x86 that would put a Google data center somewhere between the cockroach and a frog. If in 2018 Google were to upgrade the Oregon facility to Cascade Lakes it would still be only 12.8 million threads, still less than a frog. Given the geometric core growth though it will only be a few decades before Google is deploying data centers approaching the capability of the 86 billion neurons found in the human brain. Oh, and that’s assuming an x86 thread, and a neuron is even computationally similar.

So what is Neural Class Networking? As mentioned above a neuron on average is connected to 7,000 other neurons. Imagine if every hardware thread of execution in your server were networked to even 64 external threads of execution on related systems working on the same problem, that’s the start of neural class networking. Today we have servers with typically 32 threads of execution. Solarflare’s newest generation of XtremeScale Smart NICs provides 2,048 virtual NICs, so each thread of those 32 threads has the capability to sustain 64 dedicated hardware paths to other external threads. That’s the start of Neural Class Networking.

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