Read an article the other day about SpiNNaker, the University of Manchester’s neuromorphic supercomputer (see Live Science Article: Worlds largest supercomputer brain…). There’s also a wikipedia page on SpiNNaker and a SpiNNaker project page.
SpiNNaker is part of the European Union Human Brain Project (HBP), Brain Simulation Program.
SpiNNaker supercomputing hardware
(Most of the following information is from the SpiNNaker project home page and SpiNNaker architectural overview page.)
The system has 1 million ARM9 (968) cores and ~7TB of memory, with each core emulating 1000 spiking neurons. With this amount of computing power, it should be able to emulate a 1B (1 billion, 10^9) neuron brain (region).
The system will consist of 1200 PCBs with each PCB containing a 48 chip array 
and associated networking hardware & memory. Each node contains a SpiNNaker chip with its 18 ARM9 cores.
Each node has two chips stitch bonded together on top of one another. The bottom consists of the 18-ARM9 cores and the top the double DDR memory and networking layer.
Total bisectional networking bandwidth is 5 B packets/second with each packet consisting of 5 or 9 bytes of data.
SpiNNaker operates on 1W per chip or 90KW of power to run the entire machine. Given that each chip is 18 cores and each core is 1000 neurons, this means each neuron simulation takes about 55.5µW of power to run.
You can deploy a single board as IoT solution but @ ~48W per board it may be be too energy consumptive for IoT.
SpiNNaker supercomputing software
According to the home page and the Live Science article, SpiNNaker is intended to be used to model critical segments of the human brain such as the basal ganglia brain area for the EU HBP brain simulation program.
The system architecture has three tiers, a host machine (layer) which communicates with the monitor layer to start and monitor application execution and uses “ybug” to communicate, a monitor core (layer) which interacts with ybug at the host and uses “scamp” to communicate with the application processors, and the application processors (layer) consisting of the ARM cores, memory and packet networking hardware which runs the SpiNNaker Application Runtime Kernel (sark).
Applications which run on sark can consist of spiking neural networks or multi-layer perceptrons (MLP), classical deep learning neural networks.
MLP applications use back propagation and a training and inference phases, familiar to any deep learning application and uses a fixed neural network topology.- Spiking neural network applications use ongoing learning so there’s no training or inference phases (it’s always learning), use a variable network topology (reconfiguring the ARM core-packet network) and currently supports the PyNN spiking neural network simulator.
Unfortunately most of the links in the SpiNNaker project pages referring to PyNN spiking networking applications are broken. But PyNN is a Python based spiking neural network simulator that can run on a number of different hardware platforms (including sark/SpiNNaker).
Most of the AI groups I’ve talked with mention PyTorch or TensorFlow as AI frameworks of choice these days. But it’s unclear to me whether these two support spiking neural network generation/simulation.
If you want to learn more about programming SpiNNaker please check out their Software for SpiNNaker wiki page.
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As you may recall, a homo sapiens brain has an estimated 16B to 86B neurons in its average cerebral cortex (see wikipedia “animals listed by neuron count” article, for low estimate, EU’s HBP Brain Simulation page, for high estimate), which puts SpiNNaker today, at about the equivalent of less than a average tufted capuchin cerebral cortex (@1.2B neurons).
Given the above and with SpiNNaker @1B neurons, we are only 4 to 7 generations away from human equivalence. That means we have at most ~14 years left before a 128B spiking neuronal simulation machine is available.
But SpiNNaker today is based on ARM9 cores and ARM11 cores already exist. So, if they redesigned/reimplemented the chip today, it would already be 2X the core count. aake that human equivalence is only a max of 12 years away.
The mean estimate for AGI (artificial general intelligence) seems to be 2040-2050 (see wikipedia Technological Singularity article). But given what University of Manchester’s SpiNNaker is capable of doing today, I don’t think we have that long to wait.
Photo Credits: All photos/charts above are from the SpiNNaker Project pages at the University of Manchester website
Read an article in IEEE Spectrum (
We have discussed spiking NN in prior posts latest post: I
In addition, spiking NN only use neural weight values of 0 or 1, whereas normal convolutional NN operations require 8 to 16 bit numbers. So spiking NN arithmetic really only uses addition while convolutional NN need 8-16 bit multiplicative arithmetic to determine resultant weights.
The other claim is that the TENSAI chip can perform computations on the order of 10 microwatts of power per megahertz (~10 μW/MHz) using the ARM/DSP combination with voltage scaling
