CN109376853A - Echo State Networks export aixs cylinder circuit - Google Patents
Echo State Networks export aixs cylinder circuit Download PDFInfo
- Publication number
- CN109376853A CN109376853A CN201811255479.8A CN201811255479A CN109376853A CN 109376853 A CN109376853 A CN 109376853A CN 201811255479 A CN201811255479 A CN 201811255479A CN 109376853 A CN109376853 A CN 109376853A
- Authority
- CN
- China
- Prior art keywords
- value
- control module
- termination
- matrix
- buffer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Neurology (AREA)
- Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
- Complex Calculations (AREA)
Abstract
Echo State Networks export aixs cylinder circuit, are related to nerual network technique.The present invention includes: clock signal input terminal, X vector registor, first counter, first control module, second control module, first multiplier, enable signal generator, inverse matrix operation device, B matrix buffer, Y-direction amount register, the second counter, third control module, 4th control module, second multiplier, matrix multiplier, first control module, the second control module, third control module, the 4th control module are used to extract serial number and its identical element of control terminal input value from the vector registor connecting with its input terminal.Using technology of the invention, the calculating of position power is to propose that the mode of circuit calculates based on patent, input data, computing unit, memory length and operational capability determine in the circuit, so the position weight obtained by the circuit computing directly matches with hardware neural network, solves the unmatched risk of software and hardware.
Description
Technical field
The present invention relates to nerual network techniques.
Background technique
Echo state network framework is described as Fig. 1, round expression storage unit in figure, rectangular representation module.Outside per moment
Portion inputs teacher signal to (u1~uL, y1~yM), and teacher signal is vector, is stored in U cell and Y unit, random number hair respectively
Raw device module generates input weight matrix, reservoir weight matrix and feedback weight matrix at random, respectively deposit Win unit,
W unit, Wback unit, for subsequent calls.Reservoir module calculates middle layer state value x1~xK of network and is stored in X
Unit.Training module carries out the calculating of output weight and is sent into Wout unit.Yy1~yyM indicates network reality output vector.
In the prior art, power validation testing in general position is calculated by application of the CPU or GPU to confirmation, so
Afterwards in the position weight write-in hardware neural network calculated, this method can configure position power by hardware neural network and be deposited
The floating-point operation bit length of storage space length and hardware is limited, and has the unmatched risk of interface between software and hardware.
Summary of the invention
The technical problem to be solved by the invention is to provide the Echo State Networks outputs that a kind of hardware mode is realized
Aixs cylinder circuit has the characteristics that high reliability and efficient.
The present invention solve the technical problem the technical solution adopted is that, Echo State Networks export aixs cylinder circuit,
It is characterised in that it includes following units:
Clock signal input terminal, for receiving clock signal;
X vector registor, for storing the X vector that dimension is K;
First counter, input terminal connect clock signal input terminal, and the first counter has i value output end and j value defeated
Outlet exports i value and j value when for receiving clock trigger signal;
The i value initial value is 1, and using K value as a cycle, i value with each clock trigger signal circulation increase 1 until
J=K;
The j value initial value is 1, and j value increases 1 until j=K when i value is equal to K each time;
First control module, input termination X vector registor, control termination i value output end, output termination first
Buffer;
Second control module, input termination X vector registor, control termination j value output end, output termination second
Buffer;
First multiplier, two input terminal connect the first buffer and the second buffer respectively, and output termination D matrix is slow
Storage;
Enable signal generator is connect with i value output end and j value output end, and output termination inverse matrix operation device makes
Energy end, for exporting enable signal to inverse matrix operation device in i=K and j=M;
Inverse matrix operation device, for making inversion operation to the matrix of D buffer storage when receiving enable signal;
B matrix buffer, connect with the output end of inverse matrix operation device, for storing the output of inverse matrix operation device;
Y-direction amount register, for storing the Y-direction amount that dimension is M;
Second counter, input terminal connect clock signal input terminal, and the second counter has h value output end and g value defeated
Outlet;I value and j value are exported when for receiving clock trigger signal;
The h value initial value is 1, and using M value as a cycle, h value with each clock trigger signal circulation increase 1 until
G=M;
The g value initial value is 1, and g value increases 1 until g=K when h value is equal to M each time;
Third control module, input termination X vector registor, control termination h value output end, output termination third
Buffer;
4th control module, input termination Y-direction amount register, control termination g value output end, output termination the 4th
Buffer;
Second multiplier, two input terminal connect third buffer and the 4th buffer respectively, and output termination a matrix is slow
Storage;
Matrix multiplier, two input terminal connect B matrix buffer and A matrix buffer, output termination C matrix respectively
Buffer;
First control module, the second control module, third control module, the 4th control module from it for inputting
It holds and extracts serial number element identical with its control terminal input value in the vector registor of connection.
The clock trigger signal is rising edge clock.
Using technology of the invention, the calculating of position power is to propose that the mode of circuit calculates based on patent, input data, meter
It calculates unit, memory length and operational capability to determine in the circuit, so the position weight obtained by the circuit computing is straight
It connects and matches with hardware neural network, solve the unmatched risk of software and hardware.
Detailed description of the invention
Fig. 1 is echo state network architecture diagram.
Fig. 2 is training module circuit structure diagram of the invention.
Specific embodiment
Referring to fig. 2.
In Fig. 2, the mark of each section and each section corresponding relationship of the present invention are as follows:
Counter 1 --- the first counter
Counter 2 --- the second counter
X --- X vector registor
Y --- Y-direction amount register
Control module 1 --- the first control module
Control module 2 --- the second control module
Control module 3 --- third control module
Control module 4 --- the 4th control module
Temp1 --- the first buffer
Temp2 --- the second buffer
Temp3 --- third buffer
Temp4 --- the 4th buffer
A --- A matrix buffer
B --- B matrix buffer
C --- C matrix buffer
D --- D matrix buffer
dij--- D matrix element register
ahg--- A matrix element register
The present invention provides a kind of hardware implementation modes of training module, specifically include:
Clock signal input terminal, for receiving clock signal;
X vector registor, for storing the X vector that dimension is K;
First counter, input terminal connect clock signal input terminal, and the first counter has i value output end and j value defeated
Outlet exports i value and j value when for receiving clock trigger signal;
The i value initial value is 1, and using K value as a cycle, i value with each clock trigger signal circulation increase 1 until
J=K;For example, initial value i=1, after receiving a rising edge clock, i increases 1... until i=K, receives a clock again later
Rising edge, i value become initial value 1 from K, hand of a clock circular flow when being equivalent to, referred to as " circulation increases 1 ".
The j value initial value is 1, and j value increases 1 until j=K when i value is equal to K each time;
First control module, input termination X vector registor, control termination i value output end, output termination first
Buffer;
Second control module, input termination X vector registor, control termination j value output end, output termination second
Buffer;
First multiplier, two input terminal connect the first buffer and the second buffer respectively, and output end passes through D matrix
Element register connects D matrix buffer;
Enable signal generator is connect with i value output end and j value output end, and output termination inverse matrix operation device makes
Energy end, for exporting enable signal to inverse matrix operation device in i=K and j=M;
Inverse matrix operation device, for making inversion operation to the matrix of D buffer storage when receiving enable signal;
B matrix buffer, connect with the output end of inverse matrix operation device, for storing the output of inverse matrix operation device;
Y-direction amount register, for storing the Y-direction amount that dimension is M;
Second counter, input terminal connect clock signal input terminal, and the second counter has h value output end and g value defeated
Outlet;I value and j value are exported when for receiving clock trigger signal;
The h value initial value is 1, and using M value as a cycle, h value with each clock trigger signal circulation increase 1 until
G=M;
The g value initial value is 1, and g value increases 1 until g=K when h value is equal to M each time;
Third control module, input termination X vector registor, control termination h value output end, output termination third
Buffer;
4th control module, input termination Y-direction amount register, control termination g value output end, output termination the 4th
Buffer;
Second multiplier, two input terminal connect third buffer and the 4th buffer respectively, and output end passes through A matrix
Element register connects A matrix buffer;
Matrix multiplier, two input terminal connect B matrix buffer and A matrix buffer, output termination C matrix respectively
Buffer;
First control module, the second control module, third control module, the 4th control module from it for inputting
It holds and extracts serial number element identical with its control terminal input value in the vector registor of connection.
Claims (2)
1. Echo State Networks export aixs cylinder circuit, which is characterized in that including following units:
Clock signal input terminal, for receiving clock signal;
X vector registor, for storing the X vector that dimension is K;
First counter, input terminal connect clock signal input terminal, and the first counter has i value output end and j value output end,
I value and j value are exported when for receiving clock trigger signal;
The i value initial value is 1, and using K value as a cycle, and i value increases 1 until j=with each clock trigger signal circulation
K;
The j value initial value is 1, and j value increases 1 until j=K when i value is equal to K each time;
First control module, input termination X vector registor, control termination i value output end, the first caching of output termination
Device;
Second control module, input termination X vector registor, control termination j value output end, the second caching of output termination
Device;
First multiplier, two input terminal connect the first buffer and the second buffer, output termination D matrix caching respectively
Device;
Enable signal generator is connect with i value output end and j value output end, the enable end of output termination inverse matrix operation device,
For exporting enable signal to inverse matrix operation device in i=K and j=M;
Inverse matrix operation device, for making inversion operation to the matrix of D buffer storage when receiving enable signal;
B matrix buffer, connect with the output end of inverse matrix operation device, for storing the output of inverse matrix operation device;
Y-direction amount register, for storing the Y-direction amount that dimension is M;
Second counter, input terminal connect clock signal input terminal, and the second counter has h value output end and g value output end;
I value and j value are exported when for receiving clock trigger signal;
The h value initial value is 1, and using M value as a cycle, and h value increases 1 until g=with each clock trigger signal circulation
M;
The g value initial value is 1, and when h value is equal to M each time, g value increases 1 until g=K;
Third control module, input termination X vector registor, control termination h value output end, output termination third caching
Device;
4th control module, input termination Y-direction amount register, control termination g value output end, the 4th caching of output termination
Device;
Second multiplier, two input terminal connect third buffer and the 4th buffer, output termination A matrix caching respectively
Device;
Matrix multiplier, two input terminal connect B matrix buffer and A matrix buffer, output termination C matrix caching respectively
Device;
First control module, the second control module, third control module, the 4th control module are used to connect from its input terminal
Serial number element identical with its control terminal input value is extracted in the vector registor connect.
2. Echo State Networks as described in claim 1 export aixs cylinder circuit, which is characterized in that the clock triggering letter
Number be rising edge clock.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811255479.8A CN109376853B (en) | 2018-10-26 | 2018-10-26 | Echo state neural network output axon circuit |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201811255479.8A CN109376853B (en) | 2018-10-26 | 2018-10-26 | Echo state neural network output axon circuit |
Publications (2)
Publication Number | Publication Date |
---|---|
CN109376853A true CN109376853A (en) | 2019-02-22 |
CN109376853B CN109376853B (en) | 2021-09-24 |
Family
ID=65389862
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201811255479.8A Active CN109376853B (en) | 2018-10-26 | 2018-10-26 | Echo state neural network output axon circuit |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109376853B (en) |
Citations (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1981296A (en) * | 2004-04-15 | 2007-06-13 | 神经科学研究基金会 | Mobile brain-based device for use in a real world environment |
CN103678257A (en) * | 2013-12-20 | 2014-03-26 | 上海交通大学 | Positive definite matrix floating point inversion device based on FPGA and inversion method thereof |
CN105701540A (en) * | 2016-01-11 | 2016-06-22 | 清华大学 | Self-generated neural network construction method |
CN106200655A (en) * | 2016-06-27 | 2016-12-07 | 西安交通大学 | The FPGA implementation method of BTT guided missile Neural Network Inversion automatic pilot |
CN106528047A (en) * | 2015-10-08 | 2017-03-22 | 上海兆芯集成电路有限公司 | Neuro processing unit of selectively writing starting function output or accumulator value in neuro memory |
CN106650923A (en) * | 2015-10-08 | 2017-05-10 | 上海兆芯集成电路有限公司 | Neural network elements with neural memory and neural processing unit array and sequencer |
EP3182339A1 (en) * | 2015-12-17 | 2017-06-21 | Vrije Universiteit Brussel | Reservoir computing device |
CN107229967A (en) * | 2016-08-22 | 2017-10-03 | 北京深鉴智能科技有限公司 | A kind of hardware accelerator and method that rarefaction GRU neutral nets are realized based on FPGA |
US20180075344A1 (en) * | 2016-09-09 | 2018-03-15 | SK Hynix Inc. | Neural network hardware accelerator architectures and operating method thereof |
CN107862379A (en) * | 2017-07-21 | 2018-03-30 | 电子科技大学 | Neutral net FPGA |
CN108090560A (en) * | 2018-01-05 | 2018-05-29 | 中国科学技术大学苏州研究院 | The design method of LSTM recurrent neural network hardware accelerators based on FPGA |
CN108429573A (en) * | 2018-03-02 | 2018-08-21 | 合肥工业大学 | A kind of control method for the MMSE detection circuits hidden based on the time |
CN108564169A (en) * | 2017-04-11 | 2018-09-21 | 上海兆芯集成电路有限公司 | Hardware processing element, neural network unit and computer usable medium |
US20180285728A1 (en) * | 2017-04-03 | 2018-10-04 | International Business Machines Corporation | Reservoir computing system |
CN108629407A (en) * | 2017-03-23 | 2018-10-09 | 意法半导体有限公司 | Integrated form artificial neuron element apparatus |
CN108629404A (en) * | 2017-03-23 | 2018-10-09 | 意法半导体有限公司 | Circuit is not answered for integrate artificial neuron component |
EP3451239A1 (en) * | 2016-04-29 | 2019-03-06 | Cambricon Technologies Corporation Limited | Apparatus and method for executing recurrent neural network and lstm computations |
-
2018
- 2018-10-26 CN CN201811255479.8A patent/CN109376853B/en active Active
Patent Citations (17)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1981296A (en) * | 2004-04-15 | 2007-06-13 | 神经科学研究基金会 | Mobile brain-based device for use in a real world environment |
CN103678257A (en) * | 2013-12-20 | 2014-03-26 | 上海交通大学 | Positive definite matrix floating point inversion device based on FPGA and inversion method thereof |
CN106528047A (en) * | 2015-10-08 | 2017-03-22 | 上海兆芯集成电路有限公司 | Neuro processing unit of selectively writing starting function output or accumulator value in neuro memory |
CN106650923A (en) * | 2015-10-08 | 2017-05-10 | 上海兆芯集成电路有限公司 | Neural network elements with neural memory and neural processing unit array and sequencer |
EP3182339A1 (en) * | 2015-12-17 | 2017-06-21 | Vrije Universiteit Brussel | Reservoir computing device |
CN105701540A (en) * | 2016-01-11 | 2016-06-22 | 清华大学 | Self-generated neural network construction method |
EP3451239A1 (en) * | 2016-04-29 | 2019-03-06 | Cambricon Technologies Corporation Limited | Apparatus and method for executing recurrent neural network and lstm computations |
CN106200655A (en) * | 2016-06-27 | 2016-12-07 | 西安交通大学 | The FPGA implementation method of BTT guided missile Neural Network Inversion automatic pilot |
CN107229967A (en) * | 2016-08-22 | 2017-10-03 | 北京深鉴智能科技有限公司 | A kind of hardware accelerator and method that rarefaction GRU neutral nets are realized based on FPGA |
US20180075344A1 (en) * | 2016-09-09 | 2018-03-15 | SK Hynix Inc. | Neural network hardware accelerator architectures and operating method thereof |
CN108629407A (en) * | 2017-03-23 | 2018-10-09 | 意法半导体有限公司 | Integrated form artificial neuron element apparatus |
CN108629404A (en) * | 2017-03-23 | 2018-10-09 | 意法半导体有限公司 | Circuit is not answered for integrate artificial neuron component |
US20180285728A1 (en) * | 2017-04-03 | 2018-10-04 | International Business Machines Corporation | Reservoir computing system |
CN108564169A (en) * | 2017-04-11 | 2018-09-21 | 上海兆芯集成电路有限公司 | Hardware processing element, neural network unit and computer usable medium |
CN107862379A (en) * | 2017-07-21 | 2018-03-30 | 电子科技大学 | Neutral net FPGA |
CN108090560A (en) * | 2018-01-05 | 2018-05-29 | 中国科学技术大学苏州研究院 | The design method of LSTM recurrent neural network hardware accelerators based on FPGA |
CN108429573A (en) * | 2018-03-02 | 2018-08-21 | 合肥工业大学 | A kind of control method for the MMSE detection circuits hidden based on the time |
Non-Patent Citations (5)
Title |
---|
J. LI等: "Analog hardware implementation of spike-based delayed feedback reservoir computing system", 《2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)》 * |
MORANDO S等: "Reservoir Computing optimisation for PEM fuel cell fault diagnostic", 《2017 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC)》 * |
NEUSER F等: "Region-specific integration of embryonic stem cell-derived neuronal precursors into a pre-existing neuronal circuit", 《PLOS ONE》 * |
Y. LIAO等: "An FPGA Based Real Time Reservoir Computing System for Neuromorphic Processors", 《2018 3RD ASIA-PACIFIC CONFERENCE ON INTELLIGENT ROBOT SYSTEMS (ACIRS)》 * |
王洪等: "基于l_2正则化回声状态网络的模拟电路故障诊断[", 《电子器件》 * |
Also Published As
Publication number | Publication date |
---|---|
CN109376853B (en) | 2021-09-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN104539293B (en) | A kind of electric power travelling wave signal reconstructing method based on compressed sensing | |
CN110135577A (en) | The device and method of the full Connection Neural Network of training | |
CN107247859A (en) | Verification method, device, electronic equipment and the storage medium of Logic Circuit Design | |
CN103678900B (en) | A kind of network decoupling calculation method for regional distribution network real-time simulation | |
CN106250614B (en) | Data processing method suitable for FPGA platform electro-magnetic transient real-time simulation | |
CN107491811A (en) | Method and system and neural network processor for accelerans network processing unit | |
CN107944551A (en) | One kind is used for electrowetting display screen defect identification method | |
US20210056416A1 (en) | Distributed Deep Learning System | |
CN102262211A (en) | Analog circuit failure diagnosis method based on dynamic classification of echo state network | |
CN108846228A (en) | A kind of distribution line rocket triggered lightning test method that arrester routinely configures | |
Cantore et al. | Gas turbine cycle modeling oriented to component performance evaluation from limited design or test data | |
CN116707331A (en) | Inverter output voltage high-precision adjusting method and system based on model prediction | |
CN104635146A (en) | Analog circuit fault diagnosis method based on random sinusoidal signal test and HMM (Hidden Markov Model) | |
CN109376853A (en) | Echo State Networks export aixs cylinder circuit | |
CN105866563A (en) | Relay protection algorithm validation system and method thereof | |
CN109299487A (en) | Neural network model, accelerator, modeling method and device, medium and system | |
CN101976044A (en) | Wind power system modeling and DSP (Digital Signal Processor) realizing method based on neural network | |
CN113656070A (en) | Random instruction verification method and device for processor, electronic equipment and storage medium | |
CN105991289A (en) | Side channel energy analysis method and device of SM3 cipher algorithm | |
CN104536807B (en) | DC/DC real-time simulators and method based on FPGA | |
CN110472385A (en) | A kind of password cracking method and device | |
CN107861082A (en) | A kind of calibration intervals of electronic metering equipment determine method and device | |
CN104330135B (en) | A kind of continuous liquid level analog simulation method | |
Fen et al. | An industrial network protocol fuzzing framework based on deep adversarial networks | |
Ma et al. | Deep simple recurrent unit-based transient modeling method for high-speed circuits |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |