Stochastic weights binary neural networks on FPGA
Stochastic weights binary neural networks on FPGA
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FPGA 上的随机权重二元神经网络
DOI：
10.1109/isne.2018.8394726
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Takayuki Kawahara
中科院分区：
文献类型：
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作者：
Yasushi Fukuda;Takayuki Kawahara
要实现先进的物联网(IoT)，就必须将人工智能(AI)与物联网相结合。能够操作人工智能功能的紧凑型电路将对此很有用。因此，我们提出了随机权重二进制神经网络(SWBNN)。SWBNN比具有小电路的二进制神经网络(BNN)更准确。BNN可以用较小的电路实现，因为二进制计算比实数计算需要更简单的电路。然而，BNN的准确率低于实数网络。因此，所提出的SWBNN是随机行为的BNN，这使得它们比BNN更准确。此外，SWBNN仍然可以用小电路实现，因为它们执行二进制计算。结果表明，SWBNN测试数据的精度更接近学习数据的精度，而BNN测试数据的精度更接近学习数据的精度。特别是当使用CIFAR10数据库时，学习数据和测试数据之间的识别准确率差异从BNN的6%下降到SWBNN的2%。从现场可编程门阵列(FPGA)实现的结果来看，SWBNN的电路足够小，尽管它们比BNN大10%。因此，SWBNN比BNN具有更高的精度，并且引入随机权重的电路代价较低。
To achieve an advanced Internet of Things (IoT), it is necessary to combine artificial intelligence (AI) with IoT. Compact circuits that can operate AI functions will be useful for this purpose. Therefore, we propose stochastic weights binary neural networks (SWBNN). SWBNNs are more accurate than binary neural networks (BNN) with small circuits. BNNs can be realized with small circuits since binary calculation needs simpler circuits than real number calculation. However, BNNs have lower accuracy than networks with real numbers. Thus, the proposed SWBNNs are BNNs that behave stochastically, which makes them more accurate than BNNs. Moreover, SWBNNs can still be achieved with small circuits since they execute binary calculation. As a result, the accuracy for the test data of SWBNNs is closer to the accuracy for learning data than the accuracy for the test data of BNNs is. Especially when using the CIFAR10 database, the difference in the identification accuracy rate between learning data and test data decreased from 6% for BNNs to 2% for SWBNNs. From results of a field-programmable gate array (FPGA) implementation, circuits of SWBNNs are sufficiently small although they are 10% bigger than those of BNNs. Therefore, SWBNNs are more accurate than BNNs, and the circuit costs ofintroducing stochastic weights are low.