[Embench] TinyML MLPerf results
pattrsn at cs.berkeley.edu
Sun Apr 10 00:19:39 CEST 2022
On Sat, Apr 9, 2022 at 10:14 AM Ray Simar <ray.simar at rice.edu> wrote:
> Hi Dave,
> Thanks for this! I started giving it a cursory look. I notice especially
> that some of these have been run on the Arm M4, so my guess is that they
> might be in the right ball park of fitting into an embedded memory
> footprint. I’ll see if I can find out the answer to this more precisely.
> Also, I started exploring the possibility of using some of the TinyML work
> from Pete Warden at Google (now formerly at Google and now at Stanford.)
> My students have been creating code for the M4/Arduino environment using
> the TinyML framework. So we should be able to compare and contrast the two
> bodies of work and pick something appropriate for embench.
> So, I am hopeful that we can either use MLPerfTiny or something from the
> TinyML framework. Joe and I can see if we can get some students interested
> in taking a look this summer. Stay tuned.
> All the best,
> On Apr 6, 2022, at 12:47 PM, David PATTERSON <pattrsn at cs.berkeley.edu>
> The MLPerf Tiny benchmark suite is intended for the lowest power devices
> and smallest form factors, such as deeply embedded, intelligent sensing,
> and internet-of-things applications. The second round of MLPerf Tiny
> results showed tremendous growth in collaboration with submissions from
> Alibaba, Andes, hls4ml-FINN team, Plumerai, Renesas, Silicon Labs,
> STMicroelectronics, and Syntiant. Collectively, these organizations
> submitted 19 different systems with 3X more results than the first round
> and over half the results incorporating energy measurements, an impressive
> achievement for the first benchmarking round with energy measurement.
> To view the results and find additional information about the benchmarks
> please visit https://mlcommons.org/en/inference-datacenter-20/
> Embench mailing list
> Embench at lists.librecores.org
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