Asynchronous spiking neurons, the natural key to exploit temporal sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

No abstract availabl

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings

A bio-inspired image coder with temporal scalability

We present a novel bio-inspired and dynamic coding scheme for static images. Our coder aims at reproducing the main steps of the visual stimulus processing in the mammalian retina taking into account its time behavior. The main novelty of this work is to show how to exploit the time behavior of the retina cells to ensure, in a simple way, scalability and bit allocation. To do so, our main source of inspiration will be the biologically plausible retina model called Virtual Retina. Following a similar structure, our model has two stages. The first stage is an image transform which is performed by the outer layers in the retina. Here it is modelled by filtering the image with a bank of difference of Gaussians with time-delays. The second stage is a time-dependent analog-to-digital conversion which is performed by the inner layers in the retina. Thanks to its conception, our coder enables scalability and bit allocation across time. Also, our decoded images do not show annoying artefacts such as ringing and block effects. As a whole, this article shows how to capture the main properties of a biological system, here the retina, in order to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS 2011

Dynamic Quantization using Spike Generation Mechanisms

This paper introduces a neuro-inspired co-ding/decoding mechanism of a constant real value by using a Spike Generation Mechanism (SGM) and a combination of two Spike Interpretation Mechanisms (SIM). One of the most efficient and widely used SGMs to encode a real value is the Leaky-Integrate and Fire (LIF) model which produces a spike train. The duration of the spike train is bounded by a given time constraint. Seeking for a simple solution of how to interpret the spike train and to reconstruct the input value, we combine two different kinds of SIMs, the time-SIM and the rate-SIM. The time-SIM allows a high quality interpretation of the neural code and the rate-SIM allows a simple decoding mechanism by couting the spikes. The resulting coding/decoding process, called the Dual-SIM Quantizer (Dual-SIMQ), is a non-uniform quantizer. It is shown that it coincides with a uniform scalar quantizer under certain assumptions. Finally, it is also shown that the time constraint can be used to control automatically the reconstruction accuracy of this time-dependent quantizer