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smsharif1991 and prasshantg regression: Add AlexNet network with actual weights
- Update large testplan to include alexnet
- Add fbuf for AlexNet with actual weights
- Add leads for AlexNet

Change-Id: Ib9fd2c003363855a84576a82ff7b258c2259456e
Signed-off-by: Sharif Inamdar <isharif@nvidia.com>
Reviewed-by: Prashant Gaikwad <pgaikwad@nvidia.com>
Latest commit 1f44c24 Aug 7, 2018


NVDLA Open Source Software


The NVIDIA Deep Learning Accelerator (NVDLA) is a free and open architecture that promotes a standard way to design deep learning inference accelerators. With its modular architecture, NVDLA is scalable, highly configurable, and designed to simplify integration and portability. Learn more about NVDLA on the project web page.


Online Documentation

You can find the latest NVDLA SW documentation here. This README file contains only basic information.

Kernel Mode Driver

The kernel mode driver (KMD) is supported as a Linux out-of-tree kernel module. It has been verified with Linux 4.13.3 on ARM64 and is expected to work on other cpu architectures with little or no modification. The driver uses DRM and GEM PRIME for DMA buffer allocation and sharing.

Building the Linux KMD

make KDIR=<path_to_Linux_source> ARCH=arm64 CROSS_COMPILE=<path_to_toolchain>

User Mode Driver and test application

The user mode driver (UMD) includes runtime library. It provides interfaces to load network from loadable and submit it to NVDLA KMD. For reference, this package also includes test application demonstrating usage of runtime interfaces.

Building the Linux UMD and test application

export TOP=<path_to_umd>


NVDLA SW is released under the BSD 3-Clause license. An exception exists for the NVDLA SW Linux Kernel Mode Driver which is released under a GPLv2/BSD 3-Clause dual license. Each source and header file contains its license notice at the start of the file.

NVDLA SW uses some external software components such as flatbuffers, half-precision library. License text for these softwares is included in COPYING file.