hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware · HackerPapers discussions

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware — mentioned by bschup

Fri, 04 Sep 2026 16:48:00 GMT

ML inference using FPGA's is an active area of research, https://arxiv.org/html/2512.01463v1 and have been successfully used in LHC/HEP detectors such as CMS and ATLAS.

Paper and discussion history. A mention is not an endorsement.

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware — mentioned by chsun

Sat, 28 Mar 2026 16:04:40 GMT

One of the authors (of one of the two models, not this particular paper) here. Just a clarification, these models are *not* burned into silicon. They are trained with brutal QAT but are put onto fpgas. For axol1tl, the weights are burned in the sense that the weights are hard-wired in the fabric (i.e., shift-add instead of conventional read-muk-add cycle), but not on the raw silicon so the chip can be reprogrammed. Though, for projects like smartpixel or HG-Cal readout, there are similar ones targeting silicon (google something like "smartpixel cern", "HGCAL autoencoder" and you will find…

Paper and discussion history. A mention is not an endorsement.

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware — HN submission

Fri, 05 Dec 2025 16:33:58 GMT

hls4ml: A Flexible, OSS Platform for ML Acceleration on Reconfigurable Hardware

4 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.