IBM Advances Neuromorphic Computing for Deep Learning

By John Russell

September 29, 2016

Deep learning efforts today are run on standard computer hardware using convolutional neural networks. Indeed the approach has proven powerful by pioneers such as Google and Microsoft. In contrast neuromorphic computing, whose spiking neuron architecture more closely mimics human brain function, has generated less enthusiasm in the deep learning community. Now, work by IBM using its TrueNorth chip as a test case may bring deep learning to neuromorphic architectures.

Writing in the Proceedings of the National Academy of Science (PNAS) in August (Convolutional networks for fast, energy-efficient neuromorphic computing), researchers from IBM Research report, “[We] demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, perform inference while preserving the hardware’s underlying energy-efficiency and high throughput.”

The impact could be significant as neuromorphic hardware and software technology have been rapidly advancing on several fronts. IBM researchers ran the datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per watt). They report their approach allowed networks to be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. Basically, the new approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors.

“The new milestone provides a palpable proof of concept that the efficiency of brain-inspired computing can be merged with the effectiveness of deep learning, paving the path towards a new generation of chips and algorithms with even greater efficiency and effectiveness,” said Dharmendra Modha, chief scientist for brain-inspired computing at IBM Research-Almaden, in an interesting article by Jeremy Hsu on the IBM work posted this week on the IEEE Spectrum (IBM’s Brain-Inspired Chip Tested for Deep Learning.)

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. 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.

Shown here are dataset samples the researcher worked with.

As Hsu points out in the IEEE Spectrum article, “Deep-learning experts have generally viewed spiking neural networks as inefficient – at least, compared with convolutional neural networks – for the purposes of deep learning. Yann LeCun, director of AI research at Facebook and a pioneer in deep learning, previously critiqued IBM’s TrueNorth chip because it primarily supports spiking neural networks. (See IEEE Spectrum’s previous interview with LeCun on deep learning.)

“The IBM TrueNorth design may better support the goals of neuromorphic computing that focus on closely mimicking and understanding biological brains, says Zachary Chase Lipton, a deep-learning researcher in the Artificial Intelligence Group at the University of California, San Diego. By comparison, deep-learning researchers are more interested in getting practical results for AI-powered services and products.”

IBM is trying to widen that perspective. Clearly, understanding brain function better is an important element neuromorphic computing research but so, increasingly, is developing real-world applications. Lawrence Livermore National Laboratory has purchased a True-North-bases system to explore and in Europe the Human Brain Project has opened up its two big machines, SpiNNaker at Manchester University, U.K., and BrainSaleS in Germany to researchers to develop applications and explore neuromorphic computing.

The IBM paper authors describe the traditional deep learning challenge well: “Contemporary convolutional networks typically use high precision (32-bit) neurons and synapses to provide continuous derivatives and support small incremental changes to network state, both formally required for back-propagation-based gradient learning. In comparison, neuromorphic designs can use one-bit spikes to provide event-based computation and communication (consuming energy only when necessary) and can use low-precision synapses to co- locate memory with computation (keeping data movement local and avoiding off-chip memory bottlenecks).”

By introducing two constraints into the learning rule – binary-valued neurons with approximate derivatives and trinary-valued synapses – the researchers say it is possible to adapt backpropagation to create networks directly implementable using energy efficient neuromorphic dynamics.

“For structure, typical convolutional networks place no constraints on filter sizes, whereas neuromorphic systems can take advantage of blockwise connectivity that limits filter sizes, thereby saving energy because weights can now be stored in local on-chip memory within dedicated neural cores. Here, we present a convolutional network structure that naturally maps to the efficient connection primitives used in contemporary neuromorphic systems. We enforce this connectivity constraint by partitioning filters into multiple groups and yet maintain network integration by interspersing layers whose filter support region is able to cover incoming features from many groups by using a small topographic size,” write the researchers whose project was funded by DAPRA as part of its Cortical Processor program aimed at brain-inspired AI that can recognize complex patterns and adapt to changing environments,” write the researchers.

Shown below is a figure of both conventional convolutional network and the TrueNorth approach.

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 × 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 θ=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. 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 × 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 θ=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.

In the IEEE article, Modha notes TrueNorth’s general design as an advantage over those of more specialized deep-learning hardware designed to run only convolutional neural networks because it will likely allow the running of multiple types of AI networks on the same chip. He’s quoted saying, “Not only is TrueNorth capable of implementing these convolutional networks, which it was not originally designed for, but it also supports a variety of connectivity patterns (feedback and lateral, as well as feed forward) and can simultaneously implement a wide range of other algorithms.”

In their paper, the authors emphasize that their work demonstrates more generally that “the structural and operational differences between neuromorphic computing and deep learning are not fundamental and points to the richness of neural network constructs and the adaptability of backpropagation. This effort marks an important step toward a new generation of applications based on embedded neural networks.” It’s bet to read the paper in full for details of the work.

Link to Paper: http://www.pnas.org/content/early/2016/09/19/1604850113.full

Link to Jeremy Hsu’s IEEE Spectrum article: http://spectrum.ieee.org/tech-talk/computing/hardware/ibms-braininspired-chip-tested-on-deep-learning

Link to related HPCwire coverage: Think Fast – Is Neuromorphic Computing Set to Leap Forward?

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