page_title: Spiking neural network
section_title: Hardware
hierarchical_section_title: Spiking neural network / Hardware
caption_attribution_description: English: Core Top-Level Microarchitecture
context_page_description: Spiking neural networks are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not fire at each propagation cycle, but rather fire only when a membrane potential – an intrinsic quality of the neuron related to its membrane electrical charge – reaches a specific value. When a neuron fires, it generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in accordance with this signal.
  In the context of spiking neural networks, the current activation level is normally considered to be the neuron's state, with incoming spikes pushing this value higher, eventually either firing or decaying. Various coding methods exist for interpreting the outgoing spike train as a real-value number, relying on either the frequency of spikes, or the interval between spikes, to encode information.
context_section_description: Future neuromorphic architectures will comprise billions of such nanosynapses, which require a clear understanding of the physical mechanisms responsible for plasticity. Experimental systems based on ferroelectric tunnel junctions have been used to show that STDP can be harnessed from heterogeneous polarization switching. Through combined scanning probe imaging, electrical transport and atomic-scale molecular dynamics, conductance variations can be modelled by nucleation-dominated reversal of domains. Simulations show that arrays of ferroelectric nanosynapses can autonomously learn to recognize patterns in a predictable way, opening the path towards unsupervised learning.
  Neurogrid is a board that can simulate spiking neural networks directly in hardware. (Stanford University)
  SpiNNaker (Spiking Neural Network Architecture) uses ARM processors as the building blocks of a massively parallel computing platform based on a six-layer thalamocortical model. (University of Manchester)
  TrueNorth is a processor that contains 5.4 billion transistors that consumes only 70 milliwatts; most processors in personal computers contain about 1.4 billion transistors and require 35 watts or more. IBM refers to the design principle behind TrueNorth as neuromorphic computing. Its primary purpose is pattern recognition. While critics say the chip isn't powerful enough, its supporters point out that this is only the first generation, and the capabilities of improved iterations will become clear. (IBM)
  Dynamic Neuromorphic Asynchronous Processor (DYNAP) combines slow, low-power, inhomogeneous sub-threshold analog circuits, and fast programmable digital circuits. It supports reconfigurable, general-purpose, real-time neural networks of spiking neurons. This allows the implementation of real-time spike-based neural processing architectures in which memory and computation are co-localized. It solves the von Neumann bottleneck problem and enables real-time multiplexed communication of spiking events for realising massive networks. Recurrent networks, feed-forward networks, convolutional networks, attractor networks, echo-state networks, deep networks, and sensor fusion networks are a few of the possibilities.
  Loihi is a 60-mm chip 14-nm Intel chip that offers 128 cores and 130,000 neurons. It integrates a wide range of features, such as hierarchical connectivity, dendritic compartments, synaptic delays and programmable synaptic learning rules. Running a spiking convolutional form of the Locally Competitive Algorithm, Loihi can solve LASSO optimization problems with over three orders of magnitude superior energy-delay product compared to conventional solvers running on a CPU isoprocess/voltage/area. A 64 Loihi research system offers 8-million-neuron neuromorphic system. Loihi is about 1,000 times as fast as a CPU and 10,000 times as energy efficient.
  BrainScaleS is based on physical emulations of neuron, synapse and plasticity models with digital connectivity, running up to ten thousand times faster than real time. It was developed by the European Human Brain Project. The SpiNNaker system is based on numerical models running in real time on custom digital multicore chips using the ARM architecture. It provides custom digital chips, each with eighteen cores and a shared local 128 Mbyte RAM, with a total of over 1,000,000 cores.
caption: Core Top-Level Microarchitecture
exif: {}