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具有二維狀態(tài)轉(zhuǎn)移結(jié)構(gòu)的隨機邏輯及其在神經(jīng)網(wǎng)絡(luò)中的應(yīng)用

季淵,  陳文棟,  冉峰,  張金藝,  DavidLILJA

季淵, 陳文棟, 冉峰, 張金藝, DavidLILJA. 具有二維狀態(tài)轉(zhuǎn)移結(jié)構(gòu)的隨機邏輯及其在神經(jīng)網(wǎng)絡(luò)中的應(yīng)用[J]. 電子與信息學(xué)報, 2016, 38(8): 2099-2106. doi: 10.11999/JEIT151233
引用本文: 季淵, 陳文棟, 冉峰, 張金藝, DavidLILJA. 具有二維狀態(tài)轉(zhuǎn)移結(jié)構(gòu)的隨機邏輯及其在神經(jīng)網(wǎng)絡(luò)中的應(yīng)用[J]. 電子與信息學(xué)報, 2016, 38(8): 2099-2106. doi: 10.11999/JEIT151233
JI Yuan, CHEN Wendong, RAN Feng, ZHANG Jinyi, David LILJA. Stochastic Logics with Two-dimensional State Transfer Structure and Its Application in the Artificial Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2099-2106. doi: 10.11999/JEIT151233
Citation: JI Yuan, CHEN Wendong, RAN Feng, ZHANG Jinyi, David LILJA. Stochastic Logics with Two-dimensional State Transfer Structure and Its Application in the Artificial Neural Network[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2099-2106. doi: 10.11999/JEIT151233

具有二維狀態(tài)轉(zhuǎn)移結(jié)構(gòu)的隨機邏輯及其在神經(jīng)網(wǎng)絡(luò)中的應(yīng)用

doi: 10.11999/JEIT151233
  • 2.

    (上海大學(xué)微電子研究與開發(fā)中心 上海 200072) ②(上海大學(xué)機電工程與自動化學(xué)院 上海 200072) ③(明尼蘇達大學(xué)電子與計算機工程系 明尼阿波利斯 55455)

基金項目: 

國家自然科學(xué)基金(61376028)

Stochastic Logics with Two-dimensional State Transfer Structure and Its Application in the Artificial Neural Network

  • 2.

    (Microelectronic Research and Development Center, Shanghai University, Shanghai 200072, China)

Funds: 

The National Natural Science Foundation of China (61376028)

  • 摘要: 隨機計算是一種特殊的基于概率數(shù)據(jù)碼流的數(shù)學(xué)計算方法，其優(yōu)點在于可以采用非常簡單的數(shù)字邏輯完成復(fù)雜數(shù)學(xué)運算，從而大幅降低硬件實現(xiàn)成本。該文首先討論了隨機計算的基本原理和主要運算邏輯，論述了傳統(tǒng)線性狀態(tài)機的不足，并分析了一種2維狀態(tài)轉(zhuǎn)移拓撲結(jié)構(gòu)，推導(dǎo)了通過2維有限狀態(tài)機實現(xiàn)高斯函數(shù)的方法。在此基礎(chǔ)上，提出一種隨機徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)模型，其硬件實現(xiàn)成本非常低，而性能與傳統(tǒng)神經(jīng)網(wǎng)絡(luò)相當。兩類模式識別實驗結(jié)果顯示，所提出的隨機徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)的輸出值均方誤差與相應(yīng)結(jié)構(gòu)傳統(tǒng)神經(jīng)網(wǎng)絡(luò)的差別小于1.3%。FPGA實驗結(jié)果顯示，數(shù)據(jù)寬度為12位時，隨機中間神經(jīng)元的電路面積僅為傳統(tǒng)插值查表結(jié)構(gòu)的1.2%、坐標旋轉(zhuǎn)數(shù)字計算方法(CORDIC)的2%。通過改變輸入碼流長度，該神經(jīng)網(wǎng)絡(luò)可以在處理速度、功耗和準確性之間作出平衡，具有應(yīng)用靈活性，適用于對成本、功耗要求較高的應(yīng)用如嵌入式、便攜式、穿戴式設(shè)備。
    Abstract: Stochastic computing is a special algorithm that performs mathematical operations with probabilistic values of bit streams rather than traditional deterministic values. The main advantage of stochastic computing is its great simplicity of hardware arithmetic units for mathematical operations to reduce the circuit cost. This paper discusses the principle of the stochastic computing and its main arithmetic logic. It analyzes a two-dimension state transition topology structure, and discusses the Gaussian function implementation method based on the two-dimension Finite State Machin (FSM). Then, a low cost stochastic radial basis function neural network model is proposed. Results from two pattern recognition tests show that the difference of the mean squared error between the stochastic network output value and the corresponding deterministic network output value can be less than 1.3%. FPGA implementation results show that the hardware resource requirement of the proposed stochastic hidden neuron is only 1.2% of the corresponding deterministic hidden neuron with the interpolated look-up table, and is 2.0% of the CORDIC algorithm. The accuracy, speed and power of the stochastic network can be tradeoff dynamically. This network is suitable for the low cost and low power applications like embedded, portable and wearable devices.
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出版歷程
  • 收稿日期:  2015-11-03
  • 修回日期:  2016-04-08
  • 刊出日期:  2016-08-19

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