[HN Gopher] Nvidia Deep Learning Accelerator (NVDLA): free open ...
___________________________________________________________________
Nvidia Deep Learning Accelerator (NVDLA): free open inference
accelerator (2017)
Author : my123
Score : 57 points
Date : 2021-03-05 17:13 UTC (5 hours ago)
(HTM) web link (nvdla.org)
(TXT) w3m dump (nvdla.org)
| justicezyx wrote:
| A componentized DL inference hardware library + compiler suite +
| inference runtime. Overall already appears a solid foundation for
| a production system ATM. Another example of nVidia's unchallenged
| prowess in DL software ecosystem.
|
| The hardware design library could be seen as a prominent example
| of "software defined hardware". Specifically, the compnentized
| hardware library dissects DL inference into 4 high-level
| operations: convolution, pooling, activation, normalization.
| Hardware description code is provided for these 4 operations.
| Allowing practitioners to tailor their chip design to their
| application. For instance, one could remove an operation from the
| chip, and adjust the size and throughput of any one operation.
|
| Software: compiler suite, and runtime are provided for building
| and running actual DL models. Compiler suite also operate based
| on the 4 operations, and produce an IR format that models around
| the operations; and then optimized towards the target chip.
| Runtime include kernel module and user space driver to handle the
| setup of the chip runtime and accept user request of loading
| model and inference requests.
|
| 2 questions I did not find explicit answer:
|
| * Is there an inference-aware training capability?
|
| * Does the compiler suite also consider the actual hardware spec
| during its optimization?
| halotrope wrote:
| Is there any information on how to source components outside
| Nvidias Jetson ecosystem? It would be interesting to be able to
| buy such chip individually for e.g. embedded projects but there
| seem no vendors listed. Considering the DLA project is not
| particularly new I wonder if it will remain a niche appliance on
| Nvidias boards.
| sradman wrote:
| NVDLA [1] targets IoT devices:
|
| > NVDLA is available for product development as part of NVIDIA's
| Jetson Xavier NX, a small circuit board in a form factor about
| the size of a credit card which includes a 6-core ARMv8.2 64-bit
| CPU, an integrated 384-core Volta GPU with 48 Tensor Cores, and
| dual NVDLA "engines", as described in their own press release.
| NVIDIA claims the product will deliver 14 TOPS (tera operations
| per second) of compute under 10 W, but most of this likely comes
| from the GPU cores. Applications broadly include edge computing
| inference engines, including object recognition for autonomous
| driving.
|
| > NVIDIA's involvement with open hardware includes the use of
| RISC-V processors as part of their GPU product line-up.
|
| [1] https://en.wikipedia.org/wiki/NVDLA
| my123 wrote:
| The BeagleV (https://beagleboard.org/beaglev) also has an NVDLA
| in the SoC. (1 core w/ 2048 MACs at 800MHz)
| mikehollinger wrote:
| I'm actually kind of confused here; NVDLA has been around for a
| while. What is new or interesting here that changes anything? I
| did a cursory look to see if there's any new announcements, but -
| even things like the roadmap on the site show a 2017 roadmap, and
| the last commits to the hw and sw repos are 2019. Perhaps this
| needs an edit to say it's from... 2017? Or is it 2019?
| bostonpete wrote:
| Should definitely say (2017). Looking at the Wayback Machine
| from November 2017, I don't notice any differences from the
| current page.
| dang wrote:
| Added. Thanks!
| rjsw wrote:
| I only learned about NVDLA recently, from reading the specs of
| the BeagleV board that another poster has linked to.
| ojn wrote:
| This is 2+ years old, and the Github has seen zero updates. Seems
| like a dead-end effort to me.
| blintz wrote:
| The cynic in me is confused. Nvidia isn't exactly known for
| making lots of things open source. What's their angle here? Is
| the idea to take steam out of projects like TPU by making viable,
| open-source alternatives?
| tasty_freeze wrote:
| > Nvidia isn't exactly known for making lots of things open
| source.
|
| It is true nvidia drivers are not open source, but this github
| page says nvidia has 248 repositories:
| https://github.com/NVIDIA
|
| I'm not sure why the link was submitted today. The repo says it
| is 3-4 years old.
| kkielhofner wrote:
| I understand why Nvidia gets a lot of heat from the Linux
| community regarding the closed source nature of their GPU
| drivers. That said, I use them on all of my Linux machines
| and not being a FOSS purist I don't take issue with it.
| Additionally, most of the complaints come from the Linux
| desktop community. You rarely see the ML community at large
| expressing any concern. For the most part they're just trying
| to get work done and the overall CUDA ecosystem is
| tremendously useful in that regard.
|
| Very usable desktop experience, CUDA just works, the rest of
| the software ecosystem makes anything else pale by comparison
| (see the Pytorch ROCm discussion that made the HN front page
| today). Every few months I take another look at ROCm and
| frankly don't understand how anyone can take it seriously.
|
| In the FOSS/Linux community the discussion around the closed
| source nature of desktop drivers seriously discounts the
| amount of FOSS work Nvidia has done.
| pirocks wrote:
| My perception is that Nvidia in particular gets a lot of
| heat because they have bad/buggy drivers which integrate
| poorly with existing package managers/distros, not just
| closed source drivers. Anecdotally I have at least one
| issue on my home desktop which is suspect is caused by
| Nvidia drivers and poor display hotswap on a work device
| which is definetly caused by Nvidia.
| kkielhofner wrote:
| That's fair but every time the issue is raised on HN, for
| example, there are anecdotal reports all over the place.
| Given the ridiculous number of variables I will believe
| virtually anything to be true for that specific person,
| configuration, use case, etc.
|
| I understand the support for AMD's in kernel drivers (and
| Mesa), rooting for the underdog, etc but anyone who is
| being honest will acknowledge there are plenty of issues
| with their drivers and various configurations as well.
|
| Assuming it's six of one, half dozen of the other the
| fact that Nvidia drivers also bring the entire CUDA
| ecosystem to any GPU made in the last five years (or
| more) really tips the scales (overall) for the
| justification of an Nvidia hardware purchase (all other
| things being equal).
| fortran77 wrote:
| NVIDIA has a lot of open source. https://github.com/NVIDIA
|
| NVIDIA's business is to sell GPUs. This helps.
| sliken wrote:
| Seems pretty straight forward to me. Their maximum profit comes
| from selling more GPUs, instead of trying to sell a piece of
| software that has open source competition.
| tpmx wrote:
| It's not like their sales people are having a hard time
| pushing the hardware at the moment. (In case someone hasn't
| noticed: All GPUs have been sold out for months.)
| sliken wrote:
| Sure, but they have to think ahead. GPUs make sense for
| ethereum, but not bitcoin (which moved from GPUs to FPGAs
| to ASICs). Ethereum is moving from proof of work to proof
| of stake. I expect the used market to flood with used
| mining GPUs once that happens.
| modeless wrote:
| Commoditize your complement. Nvidia wants to sell neural net
| _training_ chips. Neural net _inference_ chips are a
| complement.
| lights0123 wrote:
| In the license:
|
| > "Other NVDLA Rights" includes copyright, design right (whether
| registered or unregistered), semiconductor topography (mask work)
| rights, and database rights to the NVDLA Specification and any
| Derivative Work. For the avoidance of doubt, Other NVDLA Rights
| does not include patents or trademarks.
|
| > Subject to the terms and conditions of this License, NVIDIA and
| each Contributor hereby grant to You a perpetual, worldwide, non-
| exclusive, no-charge, royalty-free, irrevocable license under the
| Other NVDLA Rights to reproduce, prepare Derivative Works of,
| publicly display, publicly perform, sublicense, and distribute
| the NVDLA Specification and such Derivative Works, and to
| commercially exploit any mask works included in the NVDLA
| Specification or such Derivative Works
|
| Why would they grant you permissions to "semiconductor
| topography" rights?
| zokier wrote:
| Because of this I imagine?
| https://en.wikipedia.org/wiki/Semiconductor_Chip_Protection_...
| shmerl wrote:
| _> a free and open architecture that promotes a standard way to
| design deep learning inference accelerators._
|
| Is it really open and standard, i.e. not using anything like
| CUDA?
___________________________________________________________________
(page generated 2021-03-05 23:02 UTC)