Convolutional networks for fast, energy-efficient neuromorphic computing

Abstract

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

Keywords: TrueNorth; convolutional network; neural network; neuromorphic.

Fig. 1.

(A) Two layers of a convolutional network. Colors (green, purple, blue, orange) designate neurons (individual boxes) belonging to the same group (partitioning the feature dimension) at the same location (partitioning the spatial dimensions). (B) A TrueNorth chip (shown far right socketed in IBM’s NS1e board) comprises 4,096 cores, each with 256 inputs, 256 neurons, and a 256 $\times$ 256 synaptic array. Convolutional network neurons for one group at one topographic location are implemented using neurons on the same TrueNorth core (TrueNorth neuron colors correspond to convolutional network neuron colors in A), with their corresponding filter support region implemented using the core’s inputs, and filter weights implemented using the core’s synaptic array. (C) Neuron dynamics showing that the internal state variable V(t) of a TrueNorth neuron changes in response to positive and negative weighted inputs. Following input integration in each tick, a spike is emitted if V(t) is greater than or equal to the threshold $\theta =1$. V(t) is reset to 0 before input integration in the next tick. (D) Convolutional network filter weights (numbers in black diamonds) implemented using TrueNorth, which supports weights with individually configured on/off state and strength assigned by lookup table. In our scheme, each feature is represented with pairs of neuron copies. Each pair connects to two inputs on the same target core, with the inputs assigned types 1 and 2, which via the look up table assign strengths of $+1$ or $-1$ to synapses on the corresponding input lines. By turning on the appropriate synapses, each synapse pair can be used to represent $-1$, 0, or $+1$.

Fig. 2.

Dataset samples. (A) CIFAR10 examples of airplane and automobile. (B) SVHN examples of the digits 4 and 7. (C) GTSRB examples of the German traffic signs for priority road and ahead only. (D) Flickr-Logos32 examples of corporate logos for FedEx and Texaco. (E) VAD example showing voice activity (red box) and no voice activity at 0 dB SNR. (F) TIMIT examples of the phonemes pcl, p, l, ah, z (red box), en, l, and ix.

Fig. 3.

Each row shows an example image from CIFAR10 (column 1) and the corresponding output of 12 typical transduction filters (columns 2–13).

Fig. 4.

Accuracy of different sized networks running on one or more TrueNorth chips to perform inference on eight datasets. For comparison, accuracy of state-of-the-art unconstrained approaches are shown as bold horizontal lines (hardware resources used for these networks are not indicated).