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.
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.
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.
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.
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.
An FPGA-based Scalable Simulation Accelerator for Tile Architectures @HEART2011Shinya Takamaeda-Y
1. An FPGA-based Scalable Simulation Accelerator called ScalableCore is presented for simulating Tile architectures like the M-Core manycore processor.
2. ScalableCore partitions the target processor across multiple FPGAs, with each FPGA representing a "ScalableCore Unit" containing part of the processor. Units are connected via a "ScalableCore Board" to simulate the entire processor faster.
3. An initial ScalableCore system was implemented to simulate the M-Core manycore processor with up to 64 cores distributed across 64 ScalableCore Units/FPGAs. This allows simulation speed to scale with the number of FPGAs used.
A CGRA-based Approachfor Accelerating Convolutional Neural NetworksShinya Takamaeda-Y
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.
Debian Linux on Zynq (Xilinx ARM-SoC FPGA) Setup Flow (Vivado 2015.4)Shinya Takamaeda-Y
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.
OpenContrail tech doc in Japanese
1.Routing architecture and implementation
2.Service chaining architecture and implementation
3.Neutron router with OpenContrail
4.HA walk
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.
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
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.
A Framework for Efficient Rapid Prototyping by Virtually Enlarging FPGA Resou...Shinya Takamaeda-Y
A Framework for Efficient Rapid Prototyping by Virtually Enlarging FPGA Resources (ReConFig2014@Cancun, Mexico)
flipSyrup, a new framework for rapid prototyping is proposed.
PyCoRAM: Yet Another Implementation of CoRAM Memory Architecture for Modern F...Shinya Takamaeda-Y
This document describes PyCoRAM, a Python-based implementation of the CoRAM memory architecture for FPGA-based computing. PyCoRAM provides a high-level abstraction for memory management that decouples computing logic from memory access behaviors. It allows defining memory access patterns using Python control threads. PyCoRAM generates an IP core that integrates with standard IP cores on Xilinx FPGAs using the AMBA AXI4 interconnect. It supports parameterized RTL design and achieves high memory bandwidth utilization of over 84% on two FPGA boards in evaluations of an array summation application.
2. 目次
n 背景:FPGAと高位合成の流行
l 逐次記述からRTLに変換する動作合成だけでOK?
l 内容に応じて異なるパラダイムを使い分けるのが重要
n 提案:マルチパラダイム型高位ハードウェア設計
l 異なるモデル(RTL・データフロー・逐次)を密結合
l 要素技術:Veriloggen.Dataflow
• オペレータオーバーロードによる
データフローを主体とした演算パイプライン設計
n 評価
l Veriloggen.Dataflowの設計効率の評価
l PyCoRAM逐次合成器を用いたマルチパラダイム方式の初期評価
n まとめ
VLD2016-02 Shinya T-Y, NAIST 2
3. FPGAと高位合成、流行ってますね
n アクセラレータとしての
FPGA
l 機械学習(深層学習)
l データセンター
l スパコン,など
n 高位合成ツールの普及
l Vivado HLSが無料で利用可能
l (Altera, Xilinx共に)
OpenCLによる設計が可能
l オープンソースの高位合成ツールも登場
• LegUp (C)
• Synthesijer (Java)
• Polyphony (Python)
VLD2016-02 Shinya T-Y, NAIST 3
Microsoft Catapult [Putnam+, ISCA'14]
4. 高位合成だけでOK?
n No.
n 動作合成では記述できない・したくない回路も多数存在
l 例)プロセッサ,タイミング・レイテンシ制約が厳しいI/F,等
n 最終的な性能チューニングにはRTLが結局必要だったり
l 例)Traxコンテストで毎回優勝する静岡大学はRTL設計
n 関連研究: プログラミング言語の記述力を活用した
RTL設計方式・ドメイン固有言語
l Chisel[Bachrach+,DAC'12]
l PyMTL[Lockhart+,MICRO’14]
l Synthesijer.Scala[三好,IEICE RECONF'15]
VLD2016-02 Shinya T-Y, NAIST 4
15. Veriloggen.Dataflow:オペレータオーバーロード
によるデータフロー設計
VLD2016-02 Shinya T-Y, NAIST 15
delay
dout3
delay
R
delay
R
delay
R
Cond
R
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Cond
0
<
R
delay
R
Cond
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01 2
delay
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delay
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delay
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0
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delay
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din1
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12
delay
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delay
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delay
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din2
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delay
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delay
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1 2
delay
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delay
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delay
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din3
R
delay
dout2
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Cond
0
Cond
02 1
dout0 dout1
2 1
delay
R
L
delay
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2 1
1 2
データフローグラフ生成
遅延レジスタが自動挿入されたパイプライン回路
パイプライン入力
パイプライン出力
17. Veriloggenとその他の処理系との違い
n 高位合成や高位DSLはソースコードそのものが回路定義
l リフレクションを利用:ソースコード情報を取得
l ソースコードを「コンパイラで」解析しデータフロー等に変換
l 回路を追加するにはソースコードを追加する必要がある
• 頻出パターンもコード化しないといけないL
l 生成元言語のサブセットのみ対応L
n Veriloggen.Dataflowは明示的にHDLを組み立てる
l リフレクション未使用:ソースコードそのもの定義は無関係
l ソースコードの「実行」によりデータフロー等を構築
l 回路の追加は明示的なオブジェクト操作で実現される
• 頻出パターンはメソッド切り出しができるJ
l 生成元言語のすべての機能を利用できるJ
l ソフトウェア文法をそのままHW化はできないL
VLD2016-02 Shinya T-Y, NAIST 17
24. PyCoRAMにおけるIPコアの作り方・でき方
n 2種類のファイルを用意する
l Verilog HDL:
計算ロジック
l Python:
データ転送制御
n PyCoRAMが自動的にIPコアのパッケージを作成
l Python-Verilog高位合成とRTL変換を自動で行う
CoramMemory1P
#(
.CORAM_THREAD_NAME("thread_name
"),
.CORAM_ID(0),
.CORAM_ADDR_LEN(ADDR_LEN),
.CORAM_DATA_WIDTH(DATA_WIDTH)
)
inst_memory
(.CLK(CLK),
.ADDR(mem_addr),
.D(mem_d),
.WE(mem_we),
.Q(mem_q)
);
VLD2016-02 Shinya T-Y, NAIST 24
25. 評価
n 本発表では2つの観点で評価
l Dataflow.Dataflowの生産性
l マルチパラダイム型高位設計環境の初期検討
n Dataflow.Dataflowの生産性について
l アプリケーション: ソーティングネットワーク
• 完全なパイプライン回路:N-sort / cycle
• N = 4, 8, 16, 32 で評価
l 参考までにLED点灯回路(Lチカ)も評価
VLD2016-02 Shinya T-Y, NAIST 25
26. 評価
n マルチパラダイム型高位設計環境の初期検討
l RTL+Dataflow+Threadを併せ持つ高位設計環境の
プロトタイプを開発
l Pythonをベースとする独立した開発済みツールを簡易的に結合
• RTL: Veriloggen
• Dataflow: Veriloggen.Dataflow
• Thread: PyCoRAM (Thread Generator部)
n 評価方法
l SLOC: Source Lines of Code
• 同一のPyCoRAM IPコアを作成するのに必要なPythonでの
コード行数と従来手法(Verilog HDL+Python)の行数を比較
l 回路規模・最大動作周波数(見積もり)
• 合成ツール: Xilinx ISE 14.7
• デバイス: Airtix-7 XC7A100T-1csg324
VLD2016-02 Shinya T-Y, NAIST 26
27. 評価アプリケーション
n 密行列積 PyCoRAM IPコア
l 従来はRTL・Dataflow部はVerilog HDLにて実装
l 本評価ではPythonにて実装しVerilog HDLを生成
VLD2016-02 Shinya T-Y, NAIST 27
Computing Logic
Control
Thread
(Python)
sum
CoRAM
Memory 0
B
× +
CoRAM
Memory 1
CoRAM
Memory 2
Control Logic
CoRAM
Channel 0
8-stage
Multiply
PipelineA
C
Dataflow
Thread
RTL
28. 評価結果: Dataflowの生産性
n 単一のPythonコードから異なる構成のハードウェアを
n 単一のPythonコードから異なる構成のハードウェアを
生成可能
l 45行のPythonコードから4種類のソート回路を生成
l 遅延レジスタ・ストール用回路等は自動で挿入される
l →高い生産性を持つと言って良いのでは?
VLD2016-02 Shinya T-Y, NAIST 28
Python Verilog
29. 評価結果: マルチパラダイム方式の初期検討
n 単一のPythonコードから異なる構成のハードウェアを
n RTL+Dataflow: 4倍程度の長さのVerilogコードを生成
l 手書きのものと比べての遜色ないコード長
n 回路規模については若干削減
l Dataflowにより合成されたパイプラインの構成が若干異なる?
VLD2016-02 Shinya T-Y, NAIST 29
Python Verilog
手書きVerilog