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Title: Deep Rewiring: Training very sparse deep networks

Paper content:
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.
But also generic hardware and software implementations of deep learning run more efficiently for sparse networks.
Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints.
We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network.
DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded.
We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance.
DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.
Network connectivity is one of the main determinants for whether a neural network can be efficiently implemented in hardware or simulated in software.
For example, it is mentioned in Jouppi et al. (2017) that in Google's tensor processing units (TPUs), weights do not normally fit in on-chip memory for neural network applications despite the small 8 bit weight precision on TPUs.
Memory is also the bottleneck in terms of energy consumption in TPUs and FPGAs (Han et al., 2017; Iandola et al., 2016) .
For example, for an implementation of a long short term memory network (LSTM), memory reference consumes more than two orders of magnitude more energy than ALU operations (Han et al., 2017) .
The situation is even more critical in neuromorphic hardware, where either hard upper bounds on network connectivity are unavoidable (Schemmel et al., 2010; Merolla et al., 2014) or fast on-chip memory of local processing cores is severely limited, for example the 96 MByte local memory of cores in the SpiNNaker system (Furber et al., 2014) .
This implementation bottleneck will become even more severe in future applications of deep learning when the number of neurons in layers will increase, causing a quadratic growth in the number of connections between them.Evolution has apparently faced a similar problem when evolving large neuronal systems such as the human brain, given that the brain volume is dominated by white matter, i.e., by connections between neurons.
The solution found by evolution is convincing.
Synaptic connectivity in the brain is highly dynamic in the sense that new synapses are constantly rewired, especially during learning (Holtmaat et al., 2005; Stettler et al., 2006; BID0 BID2 .
In other words, rewiring is an integral part of the learning algorithms in the brain, rather than a separate process.We are not aware of previous methods for simultaneous training and rewiring in artificial neural networks, so that they are able to stay within a strict bound on the total number of connections throughout the learning process.
There are however several heuristic methods for pruning a larger network (Han et al., 2015b; a; BID4 Yang et al., 2015; Srinivas & Babu, 2015) , that is, the network is first trained to convergence, and network connections and / or neurons are pruned only subsequently.
These methods are useful for downloading a trained network on neuromorphic hardware, but not for on-chip training.
A number of methods have been proposed that are capable of reducing connectivity during training BID4 Jin et al., 2016; Narang et al., This training goal was explored by Welling & Teh (2011) , BID3 , and BID6 where it was shown that gradient descent in combination with stochastic weight updates performs Markov Chain Monte Carlo (MCMC) sampling from the posterior distribution.
In this paper we extend these results by
(a) allowing the algorithm also to sample the network structure, and
(b) including a hard posterior constraint on the total number of connections during the sampling process.
We define the training goal as follows:produce samples θ with high probability in p * (θ) = 0 if θ violates the constraint DISPLAYFORM0 where Z is a normalizing constant.
The emerging learning dynamics jointly samples from a posterior distribution over network parameters θ and constrained network architectures.
In the next section we introduce the algorithm and in Section 4 we discuss the theoretical guarantees.The DEEP R algorithm: In many situations, network connectivity is strictly limited during training, for instance because of hardware memory limitations.
Then the limiting factor for a training algorithm is the maximal connectivity ever needed during training.
DEEP R guarantees such a hard limit.
DEEP R achieves the learning goal (1) on network configurations, that is, it not only samples the network weights and biases, but also the connectivity under the given constraints.
This is achieved by introducing the following mapping from network parameters θ to network weights w:A connection parameter θ k and a constant sign s k ∈ {−1, 1} are assigned to each connection k.
If θ k is negative, we say that the connection k is dormant, and the corresponding weight is w k = 0. Otherwise, the connection is considered active, and the corresponding weight is w k = s k θ k .
Hence, each θ k encodes
(a) whether the connection is active in the network, and
(b) the weight of the connection if it is active.
Note that we use here a single index k for each connection / weight instead of the more usual double index that defines the sending and receiving neuron.
This connectioncentric indexing is more natural for our rewiring algorithms where the connections are in the focus rather than the neurons.
Using this mapping, sampling from the posterior over θ is equivalent to sampling from the posterior over network configurations, that is, the network connectivity structure and the network weights.
while number of active connections lower than K do 7 select a dormant connection k with uniform probability and activate it; 8 θ k ← 0 9 end 10 end Algorithm 1: Pseudo code of the DEEP R algorithm.
ν k is sampled from a zero-mean Gaussian of unit variance independently for each active and each update step.
Note that the gradient of the error E X,Y * (θ) is computed by backpropagation over a mini-batch in practice.DEEP R is defined in Algorithm 1. Gradient updates are performed only on parameters of active connections (line 3).
The derivatives of the error function ∂ ∂θ k E X,Y * (θ) can be computed in the usual way, most commonly with the backpropagation algorithm.
Since we consider only classification problems in this article, we used the cross-entropy error for the experiments in this article.
The third term in line 3 (−ηα) is an 1 regularization term, but other regularizers could be used as well.A conceptual difference to gradient descent is introduced via the last term in line 3.
Here, noise √ 2ηT ν k is added to the update, where the temperature parameter T controls the amount of noise and ν k is sampled from a zero-mean Gaussian of unit variance independently for each parameter and each update step.
The last term alone would implement a random walk in parameter space.
Hence, the whole line 3 of the algorithm implements a combination of gradient descent on the regularized error function with a random walk.
Our theoretical analysis shows that this random walk behavior , test classification accuracy after training for various connectivity levels (middle) and example test accuracy evolution during training (bottom) for a standard feed forward network trained on MNIST (A) and a CNN trained on CIFAR-10 (B).
Accuracies are shown for various algorithms.
Green: DEEP R; red: soft-DEEP R; blue: SGD with initially fixed sparse connectivity; dashed gray: SGD, fully connected.
Since soft-DEEP R does not guarantee a strict upper bound on the connectivity, accuracies are plotted against the highest connectivity ever met during training (middle panels).
Iteration number refers to the number of parameter updates during training.has an important functional consequence, see the paragraph after the next for a discussion on the theoretical properties of DEEP R.The rewiring aspect of the algorithm is captured in lines 4 and 6-9 in Algorithm (1).
Whenever a parameter θ k becomes smaller than 0, the connection is set dormant, i.e., it is deleted from the network and no longer considered for updates (line 4).
For each connection that was set to the dormant state, a new connection k is chosen randomly from the uniform distribution over dormant connections, k is activated and its parameter is initialized to 0. This rewiring strategy
(a) ensures that exactly K connections are active at any time during training (one initializes the network with K active connections), and
(b) that dormant connections do not need any computational demands except for drawing connections to be activated.
Note that for sparse networks, it is efficient to keep only a list of active connections and none for the dormant connections.
Then, one can efficiently draw connections from the whole set of possible connections and reject those that are already active.
Related Work: de Freitas et al. FORMULA2 considered sequential Monte Carlo sampling to train neural networks by combining stochastic weight updates with gradient updates.
Stochastic gradient updates in mini-batch learning was considered in Welling & Teh (2011) , where also a link to the true posterior distribution was established.
BID3 proposed a momentum scheme and temperature annealing (for the temperature T in our notation) for stochastic gradient updates, leading to a stochastic optimization method.
DEEP R extends this approach by using stochastic gradient Monte Carlo sampling not only for parameter updates but also to sample the connectivity of the network.
In addition, the posterior in DEEP R is subject to a hard constraint on the network architecture.
In this sense, DEEP R performs constrained sampling, or constrained stochastic optimization if the temperature is annealed.
Patterson & Teh (2013) considered the problem of stochastic gradient dynamics constrained to the probability simplex.
The methods considered there are however not readily applicable to the problem of constraints on the connection matrix considered here.
Additionally, we show that a correct sampler can be constructed that does not simulate dormant connections.
This sampler is efficient for sparse connection matrices.
Thus, we developed a novel method, random reintroduction of connections, and analyzed its convergence properties (see Theorem 2 in Appendix D).
We have presented a method for modifying backprop and backprop-through-time so that not only the weights of connections, but also the connectivity graph is simultaneously optimized during training.
This can be achieved while staying always within a given bound on the total number of connections.
When the absolute value of a weight is moved by backprop through 0, it becomes a weight with the opposite sign.
In contrast, in DEEP R a connection vanishes in this case (more precisely: becomes dormant), and a randomly drawn other connection is tried out by the algorithm.
This setup requires that, like in neurobiology, the sign of a weight does not change during learning.
Another essential ingredient of DEEP R is that it superimposes the gradient-driven dynamics of each weight with a random walk.
This feature can be viewed as another inspiration from neurobiology (Mongillo et al., 2017 ).
An important property of DEEP R is that -in spite of its stochastic ingredient -its overall learning dynamics remains theoretically tractable: Not as gradient descent in the usual sense, but as convergence to a stationary distribution of network configurations which assigns the largest probabilities to the best-performing network configurations.
An automatic benefit of this ongoing stochastic parameter dynamics is that the training process immediately adjusts to changes in the task, while simultaneously transferring previously gained competences of the network (see FIG4

assistant: text: An approach to implement deep learning directly on sparsely connected graphs, allowing networks to be trained efficiently online and for fast and flexible learning.