Interface with the Deep Learning Processor IP Core

Retrieve predictions for a batch of images or for a data stream from a live camera input by using the generated deep learning processor IP core. Select between batch processing mode and streaming mode depending on available board resources, availability of input data, and application requirements. Use MATLAB^® to run your deep learning network on the generated deep learning processor IP core and retrieve the network prediction from the generated deep learning processor IP core.

Create Deep Learning Processor Configuration

To generate a deep learning processor IP core that has the required interfaces for processing multiple data frames, create a deep learning processor configuration by using the dlhdl.ProcessorConfig class. In the deep learning processor configuration:

• Set InputRunTimeControl and OutputRunTimeControl to either port or register.

• You must set InputDataInterface and OutputDataInterface to ExternalMemory.

Use the dlhdl.buildProcessor function with the deep learning processor configuration object as the input argument to generate the deep learning processor IP core. For example, this code generates a deep learning processor IP core with the interfaces to process multiple data frames.

hPC = dlhdl.ProcessorConfig;
hPC.InputRunTimeControl = 'port';
hPC.OutputRunTimeControl = 'port'
hPC.InputDataInterface = 'External Memory';
hPC.OutputDataInterface = 'External Memory';
dlhdl.buildProcessor(hPC);

Select Data Processing Mode

Choose between batch processing mode and streaming mode based on your resource requirements, availability of inputs, and interface complexity. This table lists the different selection criteria and which mode to select based on the selection criteria.

Selection Criteria	Batch Processing Mode	Streaming Mode
Availability of input data	All input data must be available before you trigger the deep learning processor IP core to start processing data.	Stream input data as and when data is available.
Memory requirements	Can require large memory resources to store all the input data and processed output data as the deep learning processor IP core processes all the data together.	Requires minimal memory resources. The smallest memory required is twice the size of one input data frame.
Interface Complexity	Simple protocol. No handshaking protocol required.	Complex protocol. You must implement a handshaking protocol.

Design Processing Mode Interface Signals

You can group the interface signals into run-time signals, handshaking signals, and setup control signals. Handshaking signals are used only when the data processing mode is set to streaming mode.

Run-Time Control Signals

This table lists the run-time control signals, data types, interface types, and description. The interface type depends on the RunTimeControl settings. For example, if RunTimeControl is set to port, the interface type is port.

Signal Name	Data Type	Configuration Control Parameter or	Interface Type (Port or Register)	Description	Interface Direction (Input or Output)
InputStart	logical	RunTimeControl	port/register	Signal from the user to the deep learning processor IP core to start processing the data.	Input
FrameCount	integer	RunTimeControl	port/register	Signal from the user to the deep learning processor IP core specifying the number of input data frames.	Input
InputStop	logical	RunTimeControl	port/register	Signal to stop the continuous streaming mode. To stop the continuous streaming mode, set this signal to true.	Input

Run-Time Status Signals

This table lists the run-time control signals, data types, interface types, and description. The interface type depends on the RunTimeStatus settings. For example, if RunTimeStatus is set to port, the interface type is port.

Signal Name	Data Type	Configuration Control Parameter or	Interface Type (Port or Register)	Description	Interface Direction (Input or Output)
Done	logical	RunTimeStatus	port/register	Signal indicating that the deep learning processor IP core has processed all input data and written the last output to memory.	Output
StreamingDone	logical	RunTimeStatus	port/register	Signal to test streaming mode. During testing, the signal becomes true when you retrieve the last output.	Output

Handshaking signals

This table lists the handshaking signals, data types, interface types, and description. These signals are used for streaming mode. The interface type depends on the InputRunTimeControl and OutputRunTimeControl settings. For example, if InputRunTimeControl is set to port, the interface type is port. To ensure proper functionality of the generated deep learning processor, you must specify the values for the signals that are ports or registers.

Signal Name	Data Type	Configuration Control Parameter or	Interface Type (Port or Register)	Description	Interface Direction (Input or Output)
InputAddr	uint32	InputStreamControl	port/register	Signal indicating the address location in memory for loading the input data. Use this signal when the InputValid signal is high.	Output
InputNext	logical	InputStreamControl	port/register	Signal to the deep learning processor IP core to indicate that the next data frame is available for processing. Use this signal when the InputValid signal is high.	Input
InputSize	uint32	InputStreamControl	port/register	Signal indicating the size in bytes of the next input data frame. Use this signal when the InputValid signal is high. The InputSize data includes the zero padding applied to the input data.	Output
InputValid	logical	InputStreamControl	port/register	Signal from the deep learning processor IP core indicating that the input data is valid.	Output
OutputAddr	uint32	OutputStreamControl	port/register	Signal indicating the address location in memory from where to retrieve the output data. Use this signal when the OutputValid signal is high.	Output
OutputNext	logical	OutputStreamControl	port/register	Signal to the deep learning processor IP core to indicate that you have read the current output data frame. Use this signal when the OutputValid signal is high.	Input
OutputSize	uint32	OutputStreamControl	port/register	Signal indicating the size of the next output data frame in bytes. Use this signal when the OutputValid signal is high.	Output
OutputValid	logical	OutputStreamControl	port/register	Signal from the deep learning processor IP core indicating that the output data is valid.	Output

Setup Control Signals

This table lists the setup control signals, data types, interface types, and description. The interface type depends on the SetupControl settings. For example, if SetupControl is set to port, the interface type is port.

Signal Name	Data Type	Configuration Control Parameter	Interface Type (Port or Register)	Description	Interface Direction (Input or Output)
StreamingMode	logical	SetupControl	port/register	Signal from the user to the deep learning processor IP core specifying the data processing mode. false selects buffer mode and true selects streaming mode.	Input
UseCustomBaseAddr	logical	SetupControl	port/register	Signal from the user to the deep learning processor IP core to use the customer specified input and output base addresses.true selects user addresses and false selects compiler generated addresses.	Input
InputBaseAddr	uint32	SetupControl	port/register	User provided input base address. Specify the address before you toggle the InputStart signal.	Input
OutputBaseAddr	uint32	SetupControl	port/register	User provided output base address. Specify the address before you toggle the InputStart signal.	Input

Debug Registers

This table lists the debug registers, data types and description. To get a list of the debug registers, see getStatusRegisterList. Use the readStatusRegister method to read information from the debug registers.

Debug Register Name	Data type	Description
DLStart	logical	Signal indicating that the deep learning processor has started.
CONVLayerStartCount	uint	Signal indicating the total number of frames for all the convolution layers in the network.
CONVLayerEndCount	uint	Signal indicating the total number of frames processed by the convolution layers in the network.
CONVLayerActive	logical	Signal indicating that the conv layer is active and processing data
FCLayerStartCount	uint	Signal indicating the total number of frames for all the fully connected layers in the network.
FCLayerEndCount	uint	Signal indicating the total number of frames processed by the fully connected layers in the network.
FCLayerActive	logical	Signal indicating that the FC layer is active and processing data
FCLayerStartCount	uint	Signal indicating the total number of frames for all the custom layers in the network.
FCLayerEndCount	uint	Signal indicating the total number of frames processed by the custom layers in the network.
FCLayerActive	logical	Signal indicating that the custom layer is active and processing data
FrameStartCount	uint	Signal indicating the current frame being processed.
FrameEndCount	uint	Signal indicating the last frame that was processed.
DLDone	logical	Signal indicating that the deep learning processor has completed processing data.

Design Batch Processing Mode Interface

When you have all your input data available and access to large double data rate (DDR) memory space, process multiple frames by using the batch processing mode. The figure shows the generated deep learning processor IP core with interface signals for the batch processing mode of operation. You use MATLAB and a dlhdl.Workflow object to run your deep learning network on the deep learning processor IP core. Retrieve the network prediction results from the deep learning processor IP core. To use batch mode, set the FrameCount register to a value greater than or equal to one.

To process a single data frame set the FrameCount register value to one. If the FrameCount is set to zero the deep learning processor runs intermittently and the Done signal does not become true.

This flowchart shows the operation of the batch processing mode.

This timing diagram shows the operation of the batch processing mode.

Load all the data frames into consecutive input DDR memory locations, toggle the inputStart signal, wait for the done signal to go high, and then read the output data from the consecutive output DDR memory locations. The clientAction signals represent your actions of loading input data and reading output data into the DDR memory.

Design Streaming Mode Interface

When your input data is streaming in, when you have access to limited DDR memory space, and when your application requires handshaking protocols, process multiple frames by using the streaming mode. The figure shows the generated deep learning processor IP core with interface signals for the streaming mode of operation. In this figure, the live camera streams data to an image preprocessing design under test (DUT) that implements the streaming mode handshaking protocol to interact with the generated deep learning processor IP core.

Date can be streamed to the deep learning processor IP core in two modes:

• Stream Data up to a frame count value— In this mode the deep learning processor processes data frames up to the value specified in FrameCount. After processing all the frames the deep learning processor IP core sets the Done signal to true. To use this mode the FrameCount must be set to a value greater than or equal to one.

  To process a single data frame set the FrameCount register value to one.

• Continuous streaming mode— In this mode the deep learning processor IP core processes data frames until you set the InputStop value to true. To use this mode the FrameCount must be set to zero.

Streaming Mode up to a Frame Count

This flowchart shows the operation of the streaming mode data processing mode. The read and write operations occur in parallel.

The value set in the InputFrameNumberLimit specifies in terms of input and output frames the space that is allocated in the DDR for the input and output ring buffers. In streaming mode, this value must be at least two. When backpressure is applied, for values larger than two the deep learning processor IP core:

• Continues to accept input data until the input ring buffer is full.

• Continues to produce output data until the output ring buffer is full.

As streaming continues, the input and output buffers fill and drain based on output backpressure and input data availability.

This flowchart shows the operation of the streaming mode up to a frame count. The read and write operations occur in parallel.

This timing diagram shows the operation of the streaming mode up to a frame count.

1. Set the InputFrameNumberLimit argument of the compile method to a value greater than two.

2. Set the StreamingMode signal to true.

3. Set the number of data frames to process in the FrameCount register.

4. Pulse the inputStart signal. These next actions can be performed in parallel:

   1. Wait for the inputValid signal to become true and then:

      • Use the inputAddr and inputSize signals to write the next input data frame to DDR memory.

      • Pulse the inputNext signal.

   2. Wait for the outputValid signal to become true and then:

      • Use the outputAddr and outputSize signals to read the processed output data frame.

      • Pulse the outputNext signal.

5. Once the deep learning processor IP core has processed all the frames it sets the done signal to true.

The clientAction signals represent your actions of loading input data and reading output data into the DDR memory.

Continuous Streaming Mode

You can continuously stream data to the deep learning processor in continuous streaming mode. To use the continuous streaming mode, set the FrameCount to zero. To stop the data processing set the InputStop signal to true. The value set in the InputFrameNumberLimit specifies in terms of input and output frames the space that is allocated in the DDR for the input and output ring buffers. When backpressure is applied, for values larger than the value in InputFrameNumberLimit the deep learning processor IP core:

• Continues to accept input data until the input ring buffer is full.

• Continues to produce output data until the output ring buffer is full.

As streaming continues, the input and output buffers fill and drain based on output backpressure and input data availability.

This flowchart shows the operation of the continuous streaming mode. The read and write operations occur in parallel.

This timing diagram shows the operation of the continuous streaming mode.

1. Set the InputFrameNumberLimit argument of the compile method to a value greater than two.

2. Set the StreamingMode signal to true.

3. Set the number of data frames to process in the FrameCount register to zero.

4. Pulse the inputStart signal. These next actions can be performed in parallel:

   1. Wait for the inputValid signal to become true and then:

      • Use the inputAddr and inputSize signals to write the next input data frame to DDR memory.

      • Pulse the inputNext signal.

   2. Wait for the outputValid signal to become true and then:

      • Use the outputAddr and outputSize signals to read the processed output data frame.

      • Pulse the outputNext signal.

5. Once you have written all the input data and read all the output data pulse the InputStop signal.

Access Data from DDR

The deep learning IP core uses the three AXI4 Master interfaces to store and process:

• Activation data

• Weight data

• Debug data

The deep learning processor reads and writes data from the DDR based on the data processing mode of operation by using these AXI4 Master interfaces.