Hardware accelerated Sora Vincent video source code [Xingmian Electronics XQ138F-EVM development board experience] (original)

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Hardware accelerated Sora Vincent video source code [Xingmian Electronics XQ138F-EVM development board experience] (original) [Copy link]

邀请：@annysky2012 @柠檬酸钠 @RCSN @dcexpert 参与回复

Hi, dear engineers, students and enthusiasts, here I come! Welcome to the mysterious world of Star Embedding! If you are an FPGA engineer or interested in embedded heterogeneous technology, then you are definitely in the right place! Today, we will explore an amazing Star Embedding development board based on TI OMAP-L138 (fixed-point/floating-point DSP C674x+ARM9) + FPGA processor.

To accelerate the text-to-video generation model Sora with FPGA, you first need to understand the complexity and computational requirements of the model. Text-to-video generation models involve deep learning algorithms such as recurrent neural networks (RNN) or Transformer for text processing, and convolutional neural networks (CNN) or generative adversarial networks (GAN) for video generation. It usually involves hardware acceleration of the computationally intensive parts of the model. The text-to-video generation model Sora contains multiple deep learning layers, such as convolutional layers, recurrent layers, attention mechanisms, etc. Due to the parallel processing capabilities of FPGAs, they are particularly suitable for accelerating these computationally intensive tasks.

FPGA acceleration is usually achieved through parallel processing, pipeline design, and optimized memory access patterns. I will use Verilog HDL to write some FPGA accelerator modules for the text-to-video generation model Sora. Please note that due to time constraints, I will only write a few simple model acceleration codes in Verilog language now, and more complex ones will be written when there is an opportunity.

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This module is just a very simplified model acceleration code, which shows the basic flow of text input, processing, video generation and output. Due to the complexity of the text-to-video generation model Sora (such as the Transformer model in deep learning), the FPGA acceleration implementation code will be quite long and highly specialized, and involve multiple levels of design. Below I will write a simplified, conceptual Verilog text-to-video conversion program to show how to build a module on the FPGA to accelerate part of the processing process:

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Because such projects usually involve complex deep learning models, a large amount of hardware logic description, and highly customized IP core design. In practical applications, TextEncoder encodes text sequences into a format suitable for neural network models, and then converts the encoded text into video frames through the VideoAccelerator hardware module. VideoAccelerator contains many parallel computing units, memory controllers, and data path structures optimized for specific text-to-video conversion models.
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Based on the above code, I will provide a modified version of the code. This version removes the instantiation of TextToVideoHardwareAccelerator and adds a placeholder for the text to video frame conversion logic. At the same time, in order to ensure the correctness of the timing, the signal feedback of the hardware accelerator processing is added in the actual design:

EEWORLDIMGTK5

Note that the above code can use a handshake signal to start the hardware accelerator and trigger the video_frame_ready signal through the done signal after the accelerator completes the processing.

Now let me explain the code in my own words:

Module name: TextToVideoAccelerator, it is a super translator that turns text into video instantly! However, it is not a product of the Harry Potter Wizarding World, but is realized through FPGA technology.

This part needs to be designed based on the specific text-to-video generation model Sora (such as Transformer, etc.) and optimized into a form suitable for FPGA parallel computing, which involves the design and integration of a large number of hardware acceleration modules such as matrix operations, attention mechanisms, and convolution operations. The code of the FPGA accelerated text-to-video generation model involves a lot of hardware design details and deep learning algorithm implementation:

In the above model:

The encoder_text_to_hidden_encoder module is responsible for converting the input text sequence into a continuous hidden state vector.
The multi_head_attention module performs attention operations based on the hidden states produced by the encoder, which is usually very important in Transformer models.
The decoder_hidden_to_video_decoder module uses the encoder hidden states and attention weights to generate video frames.

Each module requires detailed design, including logic synthesis, optimized layout and routing to adapt to the structure of the FPGA, and a large number of parallel computing units, memory hierarchy, and data movement strategies must be considered to effectively accelerate the text-to-video generation process. In addition, other components such as convolutional neural networks (CNNs) need to be connected to generate image frames, which are usually not done directly in the Transformer framework.

At the same time, according to the characteristics of the specific model, more internal state variables and control signals need to be added to coordinate the data flow and pipeline operations at different stages to ensure that the parallel processing capability of the FPGA is maximized while maintaining data correctness.

FPGA acceleration typically involves mapping different parts of the model onto the FPGA and using high-level synthesis tools such as Xilinx Vitis or Intel OpenVINO, followed by compilation and deployment on the FPGA.

Due to the complexity of the text-to-video generation model Sora, the specific implementation of FPGA acceleration will depend on the details of the model, the hardware resources of the FPGA, and the available high-level synthesis tools. Typically, this requires a professional team including hardware engineers, deep learning experts, and FPGA software engineers to jointly design and implement such a solution.

In order to efficiently implement a text-to-video generative model on an FPGA, you need to:

Design and implement the core algorithms of the model : including word embedding, self-attention mechanism, decoding, etc. These usually require mapping from the trained neural network model to the hardware structure through high-level synthesis tools.
Optimize memory access and data flow : Rationally utilize the FPGA's BRAM resources to store weights and intermediate results, and achieve efficient read and write operations.
Use pipeline technology : maximize the use of FPGA parallel processing capabilities and pipeline the calculation process.
Quantization and Customized IP Cores : The model is quantized to fit on the FPGA resources and may require the design of customized IP cores to perform specific operations.

In practical applications, the text-to-video generation model Sora involves more complex network structures, such as the Transformer model, and requires processing of large amounts of data and computation. Writing code for FPGA-accelerated text-to-video generation model Sora is a complex process because text-to-video generation models (such as Sora, DALL-E 2, etc.) have large computational requirements. The key to FPGA acceleration is to leverage parallel processing capabilities to optimize compute-intensive tasks. Typically, this involves mapping certain layers of the deep learning model to the logic resources of the FPGA and optimizing data transfer and computation processes.

I'll write here today...

In short, the above content depicts a scene of a fantasy world, in which a hardworking electronic craftsman "FPGA" transforms into a mysterious hardware accelerator, which transforms the incoming text information into a vivid and lively video stream step by step.

I hope the above experience can help you!

Thanks!

Haven't eaten yet
February 18, 2024

dcexpert

looks good

wangerxian

Did the code fail to post?

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wangerxian posted on 2024-2-27 09:53 Did the code fail to post?

module TextToVideoAccelerator(
input wire clk,
input wire reset,

// 文本输入接口
input wire [31:0] text_input,
input wire text_valid,
output wire text_ready,

// 视频输出接口
output wire [7:0] video_output,
output wire video_valid,
input wire video_ready
);

// Text to video generation model Sora parameters
parameter TEXT_LENGTH = 1024; // Text input length
parameter VIDEO_WIDTH = 640; // Video width
parameter VIDEO_HEIGHT = 480; // Video height
parameter PIXEL_DEPTH = 8; // Pixel depth

// Internal status and control signals
reg [31:0] internal_text_buffer [0:TEXT_LENGTH-1]; // Text buffer
reg [7:0] internal_video_frame [0:VIDEO_HEIGHT-1][0:VIDEO_WIDTH-1]; // Video frame buffer
reg text_processing; // Text processing flag
reg video_generation; // Video generation flag
reg [31:0] text_index; // Current processed text index
reg [31:0] video_index; // Current generated video pixel index
reg text_buffer_full; // Is the text buffer full
reg video_frame_ready; // Is the video frame ready

// FPGA internal processing function
always @(posedge clk or posedge reset) begin
if (reset) begin
// Reset internal state
text_processing <= 0;
video_generation <= 0;
text_index <= 0;
video_index <= 0;
text_buffer_full <= 0;
video_frame_ready <= 0;
end else begin
// Text input processing
if (text_valid && !text_ready && !text_processing && !text_buffer_full) begin
// Store text input into buffer
internal_text_buffer[text_index] <= text_input;
text_index <= text_index + 1;
if (text_index == TEXT_LENGTH - 1) begin
// Text buffer is full
text_buffer_full <= 1;
text_processing <= 1; // Start processing text
end
text_ready <= 1; // Notify external text has been received
end else if (text_ready) begin
// Clear the receive ready signal
text_ready <= 0;
end

// Text processing and video generationif
(text_processing && text_buffer_full) begin
// Implement the algorithm logic for text to video generation here
// For example, use a deep learning model to convert text to video frames
// I will write this belowfor
(int y = 0; y < VIDEO_HEIGHT; y = y + 1) begin
for (int x = 0; x < VIDEO_WIDTH; x = x + 1) begin
// The first byte of the text input determines the color of the video
frameinternal_video_frame[y][x] <= internal_text_buffer[0];
end
end
// Algorithm processing is completed, ready to output video frames
// Call the hardware acceleration module to convert text to video frames
// "Pass" the contents of the text buffer to the hardware accelerator
// TextToVideoHardwareAccelerator is a hardware module
// It receives text input and outputs video frame data
// Actual hardware acceleration module designTextToVideoHardwareAccelerator
#(
.TEXT_LENGTH(TEXT_LENGTH),
.VIDEO_WIDTH(VIDEO_WIDTH),
.VIDEO_HEIGHT(VIDEO_HEIGHT)
) accelerator (
.clk(clk),
.reset(reset),
.text_in(internal_text_buffer),
.video_frame_out(internal_video_frame)
);

// After the conversion is completed, set the video frame ready signal
video_frame_ready <= 1;
end

// Video output processing
if (video_ready && video_frame_ready) begin
// Output a pixel of the video frame
video_output <= internal_video_frame[video_index / VIDEO_WIDTH][video_index % VIDEO_WIDTH];
video_valid <= 1;
video_index <= video_index + 1;

// Check if all pixels have been output
if (video_index == (VIDEO_WIDTH * VIDEO_HEIGHT) - 1) begin
// Reset video generation flags and index
video_generation <= 0;
video_index <= 0;
video_frame_ready <= 0;
end
end else if (video_valid) begin
// Clear video valid signal
video_valid <= 0;
end
end
end
endmodule

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dcexpert posted on 2024-2-27 09:42 It looks very good