Live Demonstration: Automated DNN Deployment on the IBM HERMES Project Chip
For this demonstration, we will showcase the operation of a software stack capable of automatically deploying Matrix-Vector Matrix (MVM) operations of diverse deep learning workloads in a pipelined-manner on a phase-change memory-based analog in-memory computing chip with high-accuracy. For a real chip, each deployment step will be highlighted for a transformer-based network trained to perform an organic chemical reaction prediction task. Additionally, using an emulated mode of operation, these steps will also be highlighted for a Resnet-based network, which has been trained to perform image classification, and a hybrid CNN/LSTM network trained to infer nucleotide sequences from sequences of amplitude values measured from a sequencing device.
Authors
- Abu Sebastian (ORCID: https://orcid.org/0000-0001-5603-5243)
- Manuel Le Gallo (ORCID: https://orcid.org/0000-0003-1600-6151)
- Julian Büchel (ORCID: https://orcid.org/0000-0001-9495-7150)
- Corey Lammie (ORCID: https://orcid.org/0000-0001-5564-1356)
- Giacomo Camposampiero (ORCID: https://orcid.org/0009-0000-7315-9790)
- Athanasios Vasilopoulos (ORCID: https://orcid.org/0009-0001-9081-6139)
- William Simon
- Lionel Noussi
Institutions
- IBM Research - Zurich (CH)
Publication Details
- Published
- 2025-05-25
- DOI
- https://doi.org/10.1109/iscas56072.2025.11043202
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- HORIZON EUROPE Framework Programme