# US 2019/0318226 A1 · Deep Learning Image Processing Systems Using Modularly Connected CNN Based Integrated Circuits > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0318226 A1 - Document ID: US20190318226A1 - Status: Published application - Kind: A1 - Application number: 15/951,169 - Filed: 2018-04-12 - Published: 2019-10-17 - Earliest claimed: 2018-04-12 - Assignees: Gyrfalcon Technology Inc. - Applicants: Gyrfalcon Technology Inc. - Inventors: Lin Yang; Baohua Sun; Jason Z. Dong; Charles Jin Young - Web page: https://patentgrep.com/patent/US20190318226A1 ## CPC - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06F 13/4282: {on a serial bus, e.g. I2C bus, SPI bus (on daisy chain buses G06F13/4247)} - G06N 3/045: Combinations of networks - G06N 3/0495: Quantised networks; Sparse networks; Compressed networks - G06N 3/063: using electronic means - G06N 3/09: Supervised learning - G06F 2213/0026: PCI express - G06F 2213/0042: Universal serial bus [USB] - G06N 3/048: Activation functions - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A deep learning image processing system contains at least first and second groups of cellular neural networks (CNN) based integrated circuits (ICs). The first group and the second group are operatively connected in parallel via a network bus. CNN based ICs within each of the first and second groups are operatively connected in series via the network bus. The first group is configured for performing convolutional operations in respective portions of a deep learning model for extracting features out of a first subsection of input data. The second group is configured for performing convolutional operations in respective portions of the deep learning model for extracting features out of a second subsection of the input data. The deep learning model is divided into a plurality of consecutive portions being handled by the respective CNN based ICs. The input data is partitioned into at least first and second subsections. ## Claims ### Claim 1 A deep learning image processing system comprising: a plurality of cellular neural networks (CNN) based integrated circuits (ICs) operatively connected in series via a network bus, the CNN based ICs being configured for performing convolutional operations in respective portions of a deep learning model for extracting features out of input data, wherein the deep learning model is divided into a plurality of consecutive portions and each of the CNN based ICs comprises a plurality of CNN processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit. ### Claim 2 (depends on 1) The system of claim 1, wherein each CNN processing engine includes: a CNN processing block configured for simultaneously obtaining convolution operations results using corresponding input data and pre-trained filter coefficients; a first set of memory buffers operatively coupled to the CNN processing block for storing the corresponding input data; and a second set of memory buffers operatively coupled to the CNN processing block for storing the pre-trained filter coefficients. ### Claim 3 (depends on 1) The system of claim 1, wherein said each of the CNN based ICs is packaged in a dongle for facilitating connection with the network bus that comprises a Universal Serial Bus. ### Claim 4 (depends on 3) The system of claim 3, wherein said each of the CNN based ICs is installed in a computer system for facilitating connection with the network bus that comprises a Peripheral Component Interconnect Express bus. ### Claim 5 (depends on 1) The system of claim 1, wherein the plurality of consecutive portions of the deep learning model is defined by a user of the system. ### Claim 6 (depends on 5) The system of claim 5, wherein each of the plurality of consecutive portions of the deep learning model is separated by a pooling layer. ### Claim 7 (depends on 5) The system of claim 5, wherein each of the plurality of consecutive portions of the deep learning model includes at least one major convolutional layer group in the deep learning model. ### Claim 8 (depends on 1) The system of claim 1, wherein each of the CNN based ICs includes an input buffer that is configured for receiving an output of the immediately prior portion of the plurality of consecutive portions of the deep learning model as the input data. ### Claim 9 A deep learning image processing system comprising: a plurality of cellular neural networks (CNN) based integrated circuits (ICs) operatively connected in parallel via a network bus, the CNN based ICs being configured for performing convolutional operations in a deep learning model for extracting features out of respective subsections of input data, wherein the input data is portioned into at least first and second subsections, and wherein each of the CNN based ICs comprises a plurality of CNN processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit. ### Claim 10 (depends on 9) The system of claim 9, wherein each CNN processing engine includes: a CNN processing block configured for simultaneously obtaining convolution operations results using corresponding input data and pre-trained filter coefficients; a first set of memory buffers operatively coupled to the CNN processing block for storing the corresponding input data; and a second set of memory buffers operatively coupled to the CNN processing block for storing the pre-trained filter coefficients. ### Claim 11 (depends on 9) The system of claim 9, wherein said each of the CNN based ICs is packaged in a dongle for facilitating connection with the network bus that comprises a Universal Serial Bus. ### Claim 12 (depends on 11) The system of claim 11, wherein said each of the CNN based ICs is installed in a computer system for facilitating connection with the network bus that comprises a Peripheral Component Interconnect Express bus. ### Claim 13 (depends on 9) The system of claim 9, wherein the first subsection and the second subsection of the input data include at least one overlapped row and column at respective borders with a thickness of one-pixel. ### Claim 14 A deep learning image processing system comprising: a first group of a plurality of cellular neural networks (CNN) based integrated circuits (ICs); a second group of a plurality of CNN based ICs; the first group and the second group being operatively connected in parallel via a network bus; the plurality of CNN based ICs in the first group, being operatively connected in series via the network bus, and being configured for performing convolutional operations in respective portions of a deep learning model for extracting features out of a first subsection of input data; and the plurality of CNN based ICs in the second group, being operatively connected in series via the network bus, and being configured for performing convolutional operations in respective portions of the deep learning model for extracting features out of a second subsection of the input data. ### Claim 15 (depends on 14) The system of claim 14, wherein the deep learning model is divided into a plurality of consecutive portion being handled by the respective CNN based ICs. ### Claim 16 (depends on 14) The system of claim 14, wherein the input data is portioned into at least first and second subsections. ### Claim 17 (depends on 14) The system of claim 14, wherein each of the CNN based ICs comprises a plurality of CNN processing engines operatively coupled to at least one input/output data bus, the plurality of CNN processing engines being connected in a loop with a clock-skew circuit. ### Claim 18 (depends on 17) The system of claim 17, wherein each CNN processing engine includes: a CNN processing block configured for simultaneously obtaining convolution operations results using corresponding input data and pre-trained filter coefficients; a first set of memory buffers operatively coupled to the CNN processing block for storing the corresponding input data; and a second set of memory buffers operatively coupled to the CNN processing block for storing the pre-trained filter coefficients. ### Claim 19 (depends on 14) The system of claim 14, wherein said each of the CNN based ICs is packaged in a dongle for facilitating connection with the network bus that comprises a Universal Serial Bus. ### Claim 20 (depends on 18) The system of claim 18, wherein said each of the CNN based ICs is installed in a computer system for facilitating connection with the network bus that comprises a Peripheral Component Interconnect Express bus.