This document discusses Veriloggen, a Python framework for generating Verilog HDL code from Python. It allows designing hardware at the register-transfer level using Python by mapping Python constructs to Verilog modules, always blocks, wires, and other Verilog constructs. Veriloggen includes modules for RTL generation (Core), connecting Python threads to finite state machines (Thread), and defining streaming hardware (Stream). It aims to support a "Veriloggen for DSL X" approach to create domain-specific hardware description languages in Python.
2019/10/16
初心者向け颁罢贵の奥别产分野の强化法
CTFのweb分野を勉強しているものの本番でなかなか解けないと悩んでいないでしょうか?そんな悩みを持った方を対象に、私の経験からweb分野の強化法を解説します。
How to strengthen the CTF Web field for beginners !!
Although you are studying the CTF web field, are you worried that you can't solve it in production?
For those who have such problems, I will explain how to strengthen the web field based on my experience.
(study group) https://yahoo-osaka.connpass.com/event/149524/
2019/10/16
初心者向け颁罢贵の奥别产分野の强化法
CTFのweb分野を勉強しているものの本番でなかなか解けないと悩んでいないでしょうか?そんな悩みを持った方を対象に、私の経験からweb分野の強化法を解説します。
How to strengthen the CTF Web field for beginners !!
Although you are studying the CTF web field, are you worried that you can't solve it in production?
For those who have such problems, I will explain how to strengthen the web field based on my experience.
(study group) https://yahoo-osaka.connpass.com/event/149524/
This document discusses NNgen, a tool for generating hardware implementations of neural networks from high-level models. It can generate optimized RTL and IP-XACT from models defined using frameworks like TensorFlow or ONNX. NNgen uses the Veriloggen library for hardware synthesis from Python, generating FSMs and scheduled pipelines to implement DNN layers as hardware accelerators. It aims to bridge the gap between deep learning and hardware for deploying neural networks in embedded systems.
This document discusses NNgen, a tool for generating neural network hardware implementations from TensorFlow models. NNgen takes a TensorFlow model as input, performs optimizations, and generates an FPGA implementation including a control unit, computing units, RAM blocks, and interconnects. It outputs RTL code and an IP-XACT description of the generated neural network hardware accelerator. Diagrams show an example convolutional layer implementation generated by NNgen, including weight and activation memory blocks, multiply-accumulate units, addition trees, and reuse of computation units via a substream pool.
Veriloggen is a Python library that allows users to generate RTL from Python code for FPGA implementation. It supports threads to model hardware tasks, streams to connect hardware components, and intrinsic functions that map to RTL. The library can synthesize Python code into Verilog for FPGA synthesis and implementation, providing an easier high-level approach to developing FPGA hardware compared to writing RTL directly in Verilog.
Pythonによるカスタム可能な高位設計技術 (Design Solution Forum 2016@新横浜)Shinya Takamaeda-Y
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Veriloggen is a Python library that allows users to generate Verilog HDL code from Python. It provides objects and methods to define RTL modules in Python, including module inputs/outputs, registers, assignments, always blocks, etc. When the Veriloggen object is passed to the to_verilog() method, it traverses the object and generates equivalent Verilog HDL code. This allows rapid prototyping of RTL designs in Python without having to write low-level Verilog code directly.
The document discusses Twitter and GitHub accounts, an IPSJ conference, and hardware including an Intel Core i7, FPGA boards from Digilent and ScalableCore, and code snippets for C programs and hardware designs including for a convolutional neural network layer.
This document provides information about using high-level programming languages to generate hardware implementations on FPGAs. It discusses how high-level synthesis (HLS) can be used to synthesize register transfer level (RTL) descriptions from C/C++ or Python code. This allows hardware to be programmed at a higher level of abstraction without having to manually write RTL code. Specific HLS tools mentioned include Xilinx Vivado HLS, Altera OpenCL, Veriloggen for Python, and synthesizing hardware from languages like C, C++, Java, and Python.
Debian Linux on Zynq (Xilinx ARM-SoC FPGA) Setup Flow (Vivado 2015.4)Shinya Takamaeda-Y
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The document describes the process to set up Debian Linux on a Zynq FPGA board using a Zybo board as a reference platform. The key steps include:
1. Developing the hardware design in Vivado, including adding a CPU, GPIO for LEDs and switches, and generating a bitstream;
2. Compiling U-boot and the Linux kernel, as well as creating a device tree and root filesystem;
3. Setting up an SD card and booting the system from the SD card.
A CGRA-based Approachfor Accelerating Convolutional Neural NetworksShinya Takamaeda-Y
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The document presents an approach for accelerating convolutional neural networks (CNNs) using a coarse-grained reconfigurable array (CGRA) called EMAX. EMAX features processing elements with local memory to improve data locality and memory bandwidth utilization. CNN computations like convolutions are mapped to EMAX by assigning weight matrices to constant registers and performing numerous small matrix multiplications in parallel. Evaluation shows EMAX achieves better performance per memory bandwidth and area than GPUs for CNN workloads due to its optimization for small matrix operations.
A Framework for Efficient Rapid Prototyping by Virtually Enlarging FPGA Resou...Shinya Takamaeda-Y
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A Framework for Efficient Rapid Prototyping by Virtually Enlarging FPGA Resources (ReConFig2014@Cancun, Mexico)
flipSyrup, a new framework for rapid prototyping is proposed.
English follows Japanese.
筑波技術大学アレクサスキル開発チームがJAWS のユーザグループで登壇した内容です.視覚障害者の開発についても少しだけ触れています.
This is a presentation given by the Tsukuba University of Technology Alexa Skills Development Team at a JAWS user group. It also touches briefly on development for visually impaired people.
Guidance for beginners and experts on how to set up a Windows driver developm...Atomu Hidaka
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This explains how to build a Windows driver development environment that can be used immediately by beginners and experts alike. The author, who has extensive experience developing various Windows drivers, shows the latest and simplest ways to use Visual Studio and WDK.
5. How to Develop a Software?
Writing a software
in programming languages
Preprocess
Compile
Assemble
Link
CompilerFlow
Execution on a CPU
int main(){?
int a = 1 + 2;?
printf(“Hello %dn”, a);?
return 0;?
}?
add $t0, $t1, $t2?
li $v0, 1?
syscall?
ELF01ABF00F1...?
Executable Binary
RECONF2015-09 Shinya T-Y, NAIST 5
6. How to Develop a (FPGA) Hardware?
Writing a hardware design in HDL
(Hardware Description Language)
Synthesis
Technology Mapping
Place and Route
Bitstream Generation
EDAFlow
Configuration of the bitstream
to an FPGA
module top?
(input CLK, RST, ?
output reg [7:0] LED);?
always @(posedge CLK) begin?
LED <= LED + 1;?
end?
endmodule?
1A0C021E...?
Original HW on an FPGA
Bitstream
RECONF2015-09 Shinya T-Y, NAIST 6
7. RTL representation using Verilog HDL
RECONF2015-09 Shinya T-Y, NAIST 7
count
(32-bit)
+
Truncate
inv
32
32
32
8
8
LED
top
32
1Combinational
Circuit
Sequential
Circuit
Combinational
Circuit
Blinking LED (LEDチカチカ)