58 research outputs found
Event-Driven Deep Neural Network Hardware System for Sensor Fusion
This paper presents a real-time multi-modal spiking Deep Neural Network (DNN) implemented on an FPGA platform. The hardware DNN system, called n-Minitaur, demonstrates a 4-fold improvement in computational speed over the previous DNN FPGA system. The proposed system directly interfaces two different event-based sensors: a Dynamic Vision Sensor (DVS) and a Dynamic Audio Sensor (DAS). The DNN for this bimodal hardware system is trained on the MNIST digit dataset and a set of unique audio tones for each digit. When tested on the spikes produced by each sensor alone, the classification accuracy is around 70% for DVS spikes generated in response to displayed MNIST images, and 60% for DAS spikes generated in response to noisy tones. The accuracy increases to 98% when spikes from both modalities are provided simultaneously. In addition, the system shows a fast latency response of only 5ms
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
A dynamic reconfigurable architecture for hybrid spiking and convolutional FPGA-based neural network designs
This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators implemented in Field-Programmable Gate Array (FPGA) that can be applied in a variety of application scenarios. Although the concept of Dynamic Partial Reconfiguration (DPR) is increasingly used in NN accelerators, the throughput is usually lower than pure static designs. This work presents a dynamically reconfigurable energy-efficient accelerator architecture that does not sacrifice throughput performance. The proposed accelerator comprises reconfigurable processing engines and dynamically utilizes the device resources according to model parameters. Using the proposed architecture with DPR, different NN types and architectures can be realized on the same FPGA. Moreover, the proposed architecture maximizes throughput performance with design optimizations while considering the available resources on the hardware platform. We evaluate our design with different NN architectures for two different tasks. The first task is the image classification of two distinct datasets, and this requires switching between Convolutional Neural Network (CNN) architectures having different layer structures. The second task requires switching between NN architectures, namely a CNN architecture with high accuracy and throughput and a hybrid architecture that combines convolutional layers and an optimized Spiking Neural Network (SNN) architecture. We demonstrate throughput results from quickly reprogramming only a tiny part of the FPGA hardware using DPR. Experimental results show that the implemented designs achieve a 7× faster frame rate than current FPGA accelerators while being extremely flexible and using comparable resources
Design Space Exploration of Sparsity-Aware Application-Specific Spiking Neural Network Accelerators
Spiking Neural Networks (SNNs) offer a promising alternative to Artificial
Neural Networks (ANNs) for deep learning applications, particularly in
resource-constrained systems. This is largely due to their inherent sparsity,
influenced by factors such as the input dataset, the length of the spike train,
and the network topology. While a few prior works have demonstrated the
advantages of incorporating sparsity into the hardware design, especially in
terms of reducing energy consumption, the impact on hardware resources has not
yet been explored. This is where design space exploration (DSE) becomes
crucial, as it allows for the optimization of hardware performance by tailoring
both the hardware and model parameters to suit specific application needs.
However, DSE can be extremely challenging given the potentially large design
space and the interplay of hardware architecture design choices and
application-specific model parameters.
In this paper, we propose a flexible hardware design that leverages the
sparsity of SNNs to identify highly efficient, application-specific accelerator
designs. We develop a high-level, cycle-accurate simulation framework for this
hardware and demonstrate the framework's benefits in enabling detailed and
fine-grained exploration of SNN design choices, such as the layer-wise
logical-to-hardware ratio (LHR). Our experimental results show that our design
can (i) achieve up to reduction in hardware resources and (ii) deliver a
speed increase of up to , while requiring fewer hardware
resources compared to sparsity-oblivious designs. We further showcase the
robustness of our framework by varying spike train lengths with different
neuron population sizes to find the optimal trade-off points between accuracy
and hardware latency
- …