25,677 research outputs found
Efficient Computation in Adaptive Artificial Spiking Neural Networks
Artificial Neural Networks (ANNs) are bio-inspired models of neural
computation that have proven highly effective. Still, ANNs lack a natural
notion of time, and neural units in ANNs exchange analog values in a
frame-based manner, a computationally and energetically inefficient form of
communication. This contrasts sharply with biological neurons that communicate
sparingly and efficiently using binary spikes. While artificial Spiking Neural
Networks (SNNs) can be constructed by replacing the units of an ANN with
spiking neurons, the current performance is far from that of deep ANNs on hard
benchmarks and these SNNs use much higher firing rates compared to their
biological counterparts, limiting their efficiency. Here we show how spiking
neurons that employ an efficient form of neural coding can be used to construct
SNNs that match high-performance ANNs and exceed state-of-the-art in SNNs on
important benchmarks, while requiring much lower average firing rates. For
this, we use spike-time coding based on the firing rate limiting adaptation
phenomenon observed in biological spiking neurons. This phenomenon can be
captured in adapting spiking neuron models, for which we derive the effective
transfer function. Neural units in ANNs trained with this transfer function can
be substituted directly with adaptive spiking neurons, and the resulting
Adaptive SNNs (AdSNNs) can carry out inference in deep neural networks using up
to an order of magnitude fewer spikes compared to previous SNNs. Adaptive
spike-time coding additionally allows for the dynamic control of neural coding
precision: we show how a simple model of arousal in AdSNNs further halves the
average required firing rate and this notion naturally extends to other forms
of attention. AdSNNs thus hold promise as a novel and efficient model for
neural computation that naturally fits to temporally continuous and
asynchronous applications
SCANN: Synthesis of Compact and Accurate Neural Networks
Deep neural networks (DNNs) have become the driving force behind recent
artificial intelligence (AI) research. An important problem with implementing a
neural network is the design of its architecture. Typically, such an
architecture is obtained manually by exploring its hyperparameter space and
kept fixed during training. This approach is time-consuming and inefficient.
Another issue is that modern neural networks often contain millions of
parameters, whereas many applications and devices require small inference
models. However, efforts to migrate DNNs to such devices typically entail a
significant loss of classification accuracy. To address these challenges, we
propose a two-step neural network synthesis methodology, called DR+SCANN, that
combines two complementary approaches to design compact and accurate DNNs. At
the core of our framework is the SCANN methodology that uses three basic
architecture-changing operations, namely connection growth, neuron growth, and
connection pruning, to synthesize feed-forward architectures with arbitrary
structure. SCANN encapsulates three synthesis methodologies that apply a
repeated grow-and-prune paradigm to three architectural starting points.
DR+SCANN combines the SCANN methodology with dataset dimensionality reduction
to alleviate the curse of dimensionality. We demonstrate the efficacy of SCANN
and DR+SCANN on various image and non-image datasets. We evaluate SCANN on
MNIST and ImageNet benchmarks. In addition, we also evaluate the efficacy of
using dimensionality reduction alongside SCANN (DR+SCANN) on nine small to
medium-size datasets. We also show that our synthesis methodology yields neural
networks that are much better at navigating the accuracy vs. energy efficiency
space. This would enable neural network-based inference even on
Internet-of-Things sensors.Comment: 13 pages, 8 figure
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
When approaching a novel visual recognition problem in a specialized image
domain, a common strategy is to start with a pre-trained deep neural network
and fine-tune it to the specialized domain. If the target domain covers a
smaller visual space than the source domain used for pre-training (e.g.
ImageNet), the fine-tuned network is likely to be over-parameterized. However,
applying network pruning as a post-processing step to reduce the memory
requirements has drawbacks: fine-tuning and pruning are performed
independently; pruning parameters are set once and cannot adapt over time; and
the highly parameterized nature of state-of-the-art pruning methods make it
prohibitive to manually search the pruning parameter space for deep networks,
leading to coarse approximations. We propose a principled method for jointly
fine-tuning and compressing a pre-trained convolutional network that overcomes
these limitations. Experiments on two specialized image domains (remote sensing
images and describable textures) demonstrate the validity of the proposed
approach.Comment: BMVC 2017 ora
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy
efficiency, in order to enable the embedded system to achieve real-time
on-board object detection. Recent deep convolutional neural network based
object detectors have achieved state-of-the-art accuracy. However, such models
are trained with numerous parameters and their high computational costs and
large storage prohibit the deployment to memory and computation resource
limited systems. Low-precision neural networks are popular techniques for
reducing the computation requirements and memory footprint. Among them, binary
weight neural network (BWN) is the extreme case which quantizes the float-point
into just bit. BWNs are difficult to train and suffer from accuracy
deprecation due to the extreme low-bit representation. To address this problem,
we propose a knowledge transfer (KT) method to aid the training of BWN using a
full-precision teacher network. We built DarkNet- and MobileNet-based binary
weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car,
pedestrian and cyclist detection. The experimental results show that the
proposed method maintains high detection accuracy while reducing the model size
of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.Comment: Accepted by ICRA 201
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