An Energy-efficient Speech Classification Convolution Neural
Network Accelerator Based on FPGA and Quantization
Abstract
Deep convolution neural networks (CNN) have been shown to own unique advantages in acoustic tasks
over recurrent neural networks (RNN). However, activation data in convolution neural networks is often
indicated in floating format, which is both time-consuming and power-consuming when be computed.
Quantization method can turn activation into fix-point, replacing floating computing into faster and more
energy-saving fix-point computing. Based on this method, this article provides a design space searching
method to quantize a binary weight neural network with least accuracy loss. We also design a specific
accelerator on FPGA platform, which is high-throughput and energy-efficient compared with CPU or
RNN-based accelerators.
Keywords: energy-efficient; reconfigurable computing; FPGA; quantization; sound classification
1. Introduction
Sound classification is a typical information
analyzing task which is widely used in military
and speech controlling. Since Deng, Yu et al
introduced RNN and LSTM (Long-Short Stage
Memory) acoustic model into speech recognition
and sound classification, LSTM has reached
series of excellent performance in this area [1][2].
However, deep neural networks based on RNN
are hard to be trained and parallelized due to
complex structure and recurrent computation.
When applied in actual missions, RNN models
usually demand high performance GPU and CPU
to satisfy computing power need. Such hardware
platforms have up to hundreds of watt power
consumption, which cannot meet requirements
for energy-sensitive circumstance. On the
contrast, CNN has been found to get excellent
performance on acoustic model [3][4][5]. Audio
files can be transferred into feature maps or
feature matrixes by wave-filtering algorithms
(such as Mel Frequency Cepstral Coefficients
algorithm) [6], acoustic CNN models then run on
these maps just like input images for computer
vision models. With tiny 3x3 or 5x5 convolution
kernels, CNNs can be trained and forward faster
than RNN for convoluting operation is easier to
be parallelized and accelerated than recurrent
computation. This advantage makes it possible to
accelerate an acoustic CNN model on specific
power-efficiency hardware platforms.
Gradient descent algorithm [7], which is
sensitive to numerical fluctuation [8], is widely
applied to train deep neural networks (DNN). To
pursue best DNN performance, it is necessary to
store data in full-precision format during training
process. However, floating data format needs
longer word-length to store and more circuit parts
to compute, leading to more energy consumption
and bigger circuit designing area [9]. Also, the
computing complexity of floating-point data
makes it difficult to reduce computing cycle
counts. Floating computation, although can keep
precision well, has become the bottleneck of
power-efficiency high performance computing.
Fortunately, some works [10][11] have
proven that floating-point data is unnecessary to
CNNs’ forwarding tasks, low precision
computing can achieve similar performance as
well. These works provide quantization method,
turning weight and activation data into fix-point
data, integer data or even binary data with little
accuracy loss. Based on kinds of quantized CNN
models, there comes BNN (Binary Neural
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