• Stars
    star
    235
  • Rank 164,839 (Top 4 %)
  • Language
    C++
  • License
    Other
  • Created about 5 years ago
  • Updated 10 months ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

SystemC/C++ library of commonly-used hardware functions and components for HLS.

MatchLib

Build Status

MatchLib is a SystemC/C++ library of commonly-used hardware functions and components that can be synthesized by most commercially-available HLS tools into RTL.

Doxygen-generated documentation can be found here.

MatchLib is based on the Connections latency-insensitive channel implementation. Connections is included with the Catapult HLS tool and is available open-source on HLSLibs. Additional documentation on the Connections latency-insensitive channel implementation can be found in the Connections Guide.

Getting Started

Tool versions

MatchLib is regressed against the following tool/dependency versions:

  • gcc - 4.9.3 (with C++11)
  • systemc - 2.3.1
  • boost - 1.68.0
  • doxygen - 1.8.11
  • make - 3.82
  • catapult - 2022.1_1
  • connections - 1.2.8 (included with Catapult)
  • vcs - 2018.09-SP2-11
  • verdi - 2018.09-SP2-11
  • python - 3.4.2
  • rapidjson - v1.1.0 (included as submodule)

Environment requirements

Makefiles assume the appropriate definition of the following environment variables:

  • BOOST_HOME
  • SYSTEMC_HOME
  • CATAPULT_HOME
  • VCS_HOME
  • NOVAS_INST_DIR

In addition, the boost and systemc library locations are expected to be in LD_LIBRARY_PATH.

Build and run

C++ compile and simulate

cd cmod/<module>
make
make run 

C++ compile and simulate all

cd cmod
make -f regress_Makefile

HLS run and Verilog simulate

cd hls/<module>
make

HLS run and Verilog simulate all

cd hls
make -f regress_Makefile

Design Checker run

cd hls/<module>
make cdc

Design Checker run all

cd hls
make -f regress_Makefile RUN_CDESIGN_CHECKER=1

Directory structure

  • cmod/include/*.h contains header files for functions and classes from MatchLib
  • cmod/<module> sub-directories contain SystemC modules from MatchLib
  • cmod/examples/<module> sub-directories contain SystemC example modules
  • cmod/unittests/<module> sub-directories contain SystemC wrappers, testbenches and tests for various MatchLib functions, classes, and modules
  • hls/<module> sub-directories contain HLS scripts for modules
  • doc contains Makefiles for building Doxygen-based documentation

Preprocessor Macro Definitions

  • HLS_CATAPULT - Select if Catapult is selected as the HLS target. Catapult header files will not be included if not set. If enabled, NVINT is defined as ac_int. If disabled, NVINT is defined as sc_int. Currently MatchLib only supports Catapult, so HLS_CATAPULT must be set.
  • HLS_ALGORITHMICC - Set to enable AlgorithmicC-specific optimizations in the code.
  • HLS_STRATUS - Set to indicate that HLS is being performed with Stratus tool. This disables Catapult-specific flags and optimizations like the use of ac_types. This feature is not fully tested.
  • SC_INCLUDE_DYNAMIC_PROCESSES - Set to enable SystemC dynamic processes.
  • __SYNTHESIS__ - Set by Catapult during the HLS flow (but not during sc_verify).
  • _SYNTHESIS_ - Flag to set when calling the HLS tool during both HLS and sc_verify.
  • SKIP_LV2TYPE - Set to enable complex types to be stored as logic vectors in certain design-specific cases.
  • COV_ENABLE - Set to enable coverage collection with CTC.
  • NVHLS_VERIFY_ISVCSMX - Set for standalone VCS-MX co-simulations of SystemC with Catapult-generated RTL. Do not use in SystemC simulation or Catapult sc_verify.
  • ENABLE_SYNC_RESET - Enables synchronous, active-low reset instead of asynchronous, active-low reset in MatchLib.
  • NV_ARRAY_MAX_SPECIALIZATIONS - Set to redefine the maximum allowed vector length for nv_arrays (default 64). Larger values may result in increased compile times.

Additional macros such as AUTO_PORT, FORCE_AUTO_PORT, CONNECTIONS_ACCURATE_SIM, CONNECTIONS_FAST_SIM, CONNECTIONS_SIM_ONLY, CONN_RAND_STALL, CONN_RAND_STALL_PRINT_DEBUG, CONNECTIONS_ASSERT_ON_QUERY, and DISABLE_PACER may be used in MatchLib. These macros are primarily defined, implemented, and documented in Connections. For more detail on these macros, see the Connections documentation.

Command-line Simulation Settings

Many of the flags above are design-specific, and so are typically codified into design Makefiles. However, some of the variables pertain to different simulation modes, and it is desirable to simulate the same design under different settings for different purposes. Accordingly, we provide the following command-line environment flags for use with the cmod or hls steps:

  • SIM_MODE - Set this variable to 1 (default) to use Connections sim-accurate mode, so that Connections ports and channels simulated in SystemC closely match the cycle-by-cycle behavior of their HLS-generated RTL counterparts. Set this variable to 2 to enable a TLM-based Connections mode that is faster to simulate but does not track the cycle behavior of HLS-generated RTL as closely. Set this variable to 0 to directly simulate the synthesized representation of Connections ports and channels (not recommended, as it may result in spurious failures).
  • RAND_STALL - Set this variable to 1 to enable random stalling on Connections ports and channels. Set to 0 (default) to disable random stalling.
  • DEBUG_LEVEL - Set to control the amount of debug information printed to the command line during execution. In general, higher debug levels will enable debug information from additional modules.
  • NVHLS_RAND_SEED - Set to a number to use as a fixed seed for nvhls_rand (defaults to 0).

To accurately simulate expected RTL performance, use the default settings. For robust verification, simulate with four different modes: both SIM_MODE=1 and SIM_MODE=2, with random stalling both enabled and disabled and various random seeds.

Questions and Contributions

We welcome feedback and contributions from the open-source hardware community. If you have a question or problem, please file an issue on GitHub. To contribute bugfixes or new features, submit a pull request. For business inquiries, please contact [email protected]. For press and other inquiries, please contact Hector Marinez at [email protected].

Contributors

MatchLib originated as a project of NVIDIA Research.

Contributors to the initial open-source release (alphabetical): Jason Clemons, Christopher Fletcher, Davide Giri, Ben Keller, Brucek Khailany, Alicia Klinefelter, Evgeni Krimer, Hyoukjun Kwon, Ziyun Li, Michael Pellauer, Nathaniel Pinckney, Antonio Puglielli, Sophia Shao, Shreesha Srinath, Gopalakrishnan Srinivasan, Christopher Torng, Rangharajan Venkatesan, Sam Xi

Portions of MatchLib are derived from code in Mentor Graphics' Algorithmic C Datatypes v3.7.1 (also released under the Apache 2.0 license). See individual file headers for details.

MatchLib's back annotation feature is dependent on RapidJSON released under the MIT License.

Attribution

If used for research, please cite the DAC paper:

Brucek Khailany, Evgeni Krimer, Rangharajan Venkatesan, Jason Clemons, Joel S. Emer, Matthew Fojtik, Alicia Klinefelter, Michael Pellauer, Nathaniel Pinckney, Yakun Sophia Shao, Shreesha Srinath, Christopher Torng, Sam (Likun) Xi, Yanqing Zhang, and Brian Zimmer, "A modular digital VLSI flow for high-productivity SoC design," in Proceedings of the ACM/IEEE Design Automation Conference, June 2018.

More Repositories

1

instant-ngp

Instant neural graphics primitives: lightning fast NeRF and more
Cuda
15,102
star
2

stylegan

StyleGAN - Official TensorFlow Implementation
Python
13,882
star
3

stylegan2

StyleGAN2 - Official TensorFlow Implementation
Python
10,740
star
4

SPADE

Semantic Image Synthesis with SPADE
Python
7,518
star
5

stylegan3

Official PyTorch implementation of StyleGAN3
Python
6,108
star
6

neuralangelo

Official implementation of "Neuralangelo: High-Fidelity Neural Surface Reconstruction" (CVPR 2023)
Python
4,125
star
7

imaginaire

NVIDIA's Deep Imagination Team's PyTorch Library
Python
3,941
star
8

stylegan2-ada-pytorch

StyleGAN2-ADA - Official PyTorch implementation
Python
3,866
star
9

ffhq-dataset

Flickr-Faces-HQ Dataset (FFHQ)
Python
3,483
star
10

tiny-cuda-nn

Lightning fast C++/CUDA neural network framework
C++
3,286
star
11

eg3d

Python
3,089
star
12

MUNIT

Multimodal Unsupervised Image-to-Image Translation
Python
2,564
star
13

SegFormer

Official PyTorch implementation of SegFormer
Python
2,252
star
14

nvdiffrec

Official code for the CVPR 2022 (oral) paper "Extracting Triangular 3D Models, Materials, and Lighting From Images".
Python
2,019
star
15

few-shot-vid2vid

Pytorch implementation for few-shot photorealistic video-to-video translation.
Python
1,780
star
16

stylegan2-ada

StyleGAN2 with adaptive discriminator augmentation (ADA) - Official TensorFlow implementation
Python
1,778
star
17

FUNIT

Translate images to unseen domains in the test time with few example images.
Python
1,545
star
18

PWC-Net

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral)
Python
1,512
star
19

noise2noise

Noise2Noise: Learning Image Restoration without Clean Data - Official TensorFlow implementation of the ICML 2018 paper
Python
1,356
star
20

alias-free-gan

Alias-Free GAN project website and code
1,320
star
21

prismer

The implementation of "Prismer: A Vision-Language Model with Multi-Task Experts".
Python
1,287
star
22

DG-Net

👫 Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral) 👫
Python
1,268
star
23

nvdiffrast

Nvdiffrast - Modular Primitives for High-Performance Differentiable Rendering
C++
1,137
star
24

edm

Elucidating the Design Space of Diffusion-Based Generative Models (EDM)
Python
1,014
star
25

Deep_Object_Pose

Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)
Python
955
star
26

VoxFormer

Official PyTorch implementation of VoxFormer [CVPR 2023 Highlight]
Python
937
star
27

NVAE

The Official PyTorch Implementation of "NVAE: A Deep Hierarchical Variational Autoencoder" (NeurIPS 2020 spotlight paper)
Python
889
star
28

BundleSDF

[CVPR 2023] BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects
Python
842
star
29

ODISE

Official PyTorch implementation of ODISE: Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models [CVPR 2023 Highlight]
Python
779
star
30

GroupViT

Official PyTorch implementation of GroupViT: Semantic Segmentation Emerges from Text Supervision, CVPR 2022.
Python
679
star
31

FasterViT

[ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention
Python
664
star
32

GA3C

Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.
Python
641
star
33

denoising-diffusion-gan

Tackling the Generative Learning Trilemma with Denoising Diffusion GANs https://arxiv.org/abs/2112.07804
Python
634
star
34

genvs

610
star
35

sionna

Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Jupyter Notebook
580
star
36

curobo

CUDA Accelerated Robot Library
Python
545
star
37

FB-BEV

Official PyTorch implementation of FB-BEV & FB-OCC - Forward-backward view transformation for vision-centric autonomous driving perception
Python
518
star
38

Dancing2Music

Python
513
star
39

planercnn

PlaneRCNN detects and reconstructs piece-wise planar surfaces from a single RGB image
Python
502
star
40

pacnet

Pixel-Adaptive Convolutional Neural Networks (CVPR '19)
Python
490
star
41

CALM

Python
486
star
42

DeepInversion

Official PyTorch implementation of Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion (CVPR 2020)
Python
474
star
43

EmerNeRF

PyTorch Implementation of EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
Python
456
star
44

FAN

Official PyTorch implementation of Fully Attentional Networks
Python
454
star
45

FourCastNet

Initial public release of code, data, and model weights for FourCastNet
Python
421
star
46

GCVit

[ICML 2023] Official PyTorch implementation of Global Context Vision Transformers
Python
414
star
47

intrinsic3d

Intrinsic3D - High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (ICCV 2017)
C++
411
star
48

nvdiffmodeling

Differentiable rasterization applied to 3D model simplification tasks
Python
404
star
49

flip

A tool for visualizing and communicating the errors in rendered images.
C++
375
star
50

wetectron

Weakly-supervised object detection.
Python
355
star
51

FoundationPose

FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
JavaScript
349
star
52

nvdiffrecmc

Official code for the NeurIPS 2022 paper "Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising".
C
341
star
53

geomapnet

Geometry-Aware Learning of Maps for Camera Localization (CVPR2018)
Python
338
star
54

GLAMR

[CVPR 2022 Oral] Official PyTorch Implementation of "GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras”.
Python
329
star
55

LSGM

The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)
Python
326
star
56

ssn_superpixels

Superpixel Sampling Networks (ECCV2018)
Python
323
star
57

DiffiT

Official Repository for DiffiT: Diffusion Vision Transformers for Image Generation
315
star
58

FreeSOLO

FreeSOLO for unsupervised instance segmentation, CVPR 2022
Python
307
star
59

long-video-gan

Official PyTorch implementation of LongVideoGAN
Python
297
star
60

neuralrgbd

Neural RGB→D Sensing: Per-pixel depth and its uncertainty estimation from a monocular RGB video
Python
294
star
61

selfsupervised-denoising

High-Quality Self-Supervised Deep Image Denoising - Official TensorFlow implementation of the NeurIPS 2019 paper
Python
293
star
62

Taylor_pruning

Pruning Neural Networks with Taylor criterion in Pytorch
Python
279
star
63

timeloop

Timeloop performs modeling, mapping and code-generation for tensor algebra workloads on various accelerator architectures.
C++
278
star
64

metfaces-dataset

Python
272
star
65

few_shot_gaze

Pytorch implementation and demo of FAZE: Few-Shot Adaptive Gaze Estimation (ICCV 2019, oral)
Python
272
star
66

splatnet

SPLATNet: Sparse Lattice Networks for Point Cloud Processing (CVPR2018)
Python
268
star
67

MinVIS

Python
261
star
68

edm2

Analyzing and Improving the Training Dynamics of Diffusion Models (EDM2)
Python
261
star
69

contact_graspnet

Efficient 6-DoF Grasp Generation in Cluttered Scenes
Python
260
star
70

CenterPose

Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image (ICRA 2022)
Python
251
star
71

trajdata

A unified interface to many trajectory forecasting datasets.
Python
245
star
72

STEP

STEP: Spatio-Temporal Progressive Learning for Video Action Detection. CVPR'19 (Oral)
Python
244
star
73

sim-web-visualizer

Web Based Visualizer for Simulation Environments
Python
231
star
74

SCOPS

SCOPS: Self-Supervised Co-Part Segmentation (CVPR'19)
Python
221
star
75

UMR

Self-supervised Single-view 3D Reconstruction
Python
221
star
76

DiffRL

[ICLR 2022] Accelerated Policy Learning with Parallel Differentiable Simulation
Python
220
star
77

cule

CuLE: A CUDA port of the Atari Learning Environment (ALE)
C++
216
star
78

SSV

Pytorch implementation of SSV: Self-Supervised Viewpoint Learning from Image Collections (CVPR 2020)
Python
214
star
79

DiffPure

A new adversarial purification method that uses the forward and reverse processes of diffusion models to remove adversarial perturbations.
Python
210
star
80

latentfusion

LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
Python
197
star
81

I2SB

Python
194
star
82

nvbio

NVBIO is a library of reusable components designed to accelerate bioinformatics applications using CUDA.
C++
193
star
83

6dof-graspnet

Implementation of 6-DoF GraspNet with tensorflow and python. This repo has been tested with python 2.7 and tensorflow 1.12.
Python
186
star
84

NVBit

183
star
85

AFNO-transformer

Adaptive FNO transformer - official Pytorch implementation
Python
174
star
86

UnseenObjectClustering

Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation
Python
166
star
87

AL-MDN

Official pytorch implementation of Active Learning for deep object detection via probabilistic modeling (ICCV 2021)
Python
159
star
88

fermat

Fermat is a high performance research oriented physically based rendering system, trying to produce beautiful pictures following the mathematician’s principle of least time
C++
158
star
89

PoseCNN-PyTorch

PyTorch implementation of the PoseCNN framework
C
156
star
90

mask-auto-labeler

Python
153
star
91

mimicgen_environments

This code corresponds to simulation environments used as part of the MimicGen project.
Python
153
star
92

Bi3D

Python
150
star
93

RVT

Official Code for RVT: Robotic View Transformer for 3D Object Manipulation
Python
147
star
94

condensa

Programmable Neural Network Compression
Python
146
star
95

traffic-behavior-simulation

Python
145
star
96

learningrigidity

Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation (ECCV 2018)
Python
144
star
97

ocrodeg

document image degradation
Jupyter Notebook
142
star
98

ocropus3

Repository collecting all the submodules for the new PyTorch-based OCR System.
Shell
141
star
99

CGBN

CGBN: CUDA Accelerated Multiple Precision Arithmetic (Big Num) using Cooperative Groups
Cuda
139
star
100

PL4NN

Perceptual Losses for Neural Networks: Caffe implementation of loss layers based on perceptual image quality metrics.
Python
138
star