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A Cross-Domain Matrix Engine for Signal Processing and Deep Neural Net Inference — arXiv:2611.03503

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03503
License (as archived): CC0-1.0
Archived: 2026-11-03T10:38:15.343Z
SHA-256 of the archived text: a34d0ee2cbfd68074f88f4f48ec8dbca405035da31ec883317f8af5477738822
Stable URL of this page: https://metraipolis.com/documento/dc_ff4e83120d80

How to cite

A Cross-Domain Matrix Engine for Signal Processing and Deep Neural Net Inference — arXiv:2611.03503 — arXiv (en), https://arxiv.org/abs/2611.03503 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ff4e83120d80 (Metraipolis archive dc_ff4e83120d80, sha256 a34d0ee2cbfd68074f88f4f48ec8dbca405035da31ec883317f8af5477738822)

BibTeX

@misc{dcff4e83120d80,
  title = {A Cross-Domain Matrix Engine for Signal Processing and Deep Neural Net Inference — arXiv:2611.03503},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03503},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ff4e83120d80; sha256 a34d0ee2cbfd68074f88f4f48ec8dbca405035da31ec883317f8af5477738822},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03503. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Matrix multiplication dominates both PHY signal processing and Deep Neural Network (DNN) inference, yet these domains place opposing demands on the matrix engine. Signal processing requires low-latency execution of relatively small matrix operations, whereas DNN inference, particularly at large batch sizes, prioritizes high throughput and energy efficiency for large matrix multiplications. While offering efficient memory access profile, a Systolic Array (SA) adds fill and drain delays per matrix. Those can be amortized for large matrices but not for small matrices. Conversely, a tensor unit completes the kernel in few cycles but would require repeated reads of same operand tiles over large matrices. This makes SA too slow for signal processing and tensor too inefficient for AI. We show that this need not be a choice between systolic and tensor structuring of Multiply-Accumulate (MAC) units. We propose a single engine that operates primarily as a SA and, through low-cost reconfiguration, transforms into a tensor-style engine on a per-operation basis. MUXes create both the spatial reduction adder tree and temporal reduction accumulators from the existing MACs and registers within the systolic array. The proposed reconfigurable SA simulated using modified SCALESim simulator, verified on an Zynq 702 FPGA and synthesized using ASAP 7nm FinFET library. SCALE-Sim simulations shows up to 7.5x lower latency for small complex value PHY kernels. Even on DNN workloads, it achieves up to 1.65x speedups over vanilla SA execution with trivial increase of memory accesses, retaining the systolic array's memory access advantage. Synthesis results estimate reconfigurable fabric costs only 8% power and 13.6% area overheads.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Hardware Architecture (cs.AR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03503 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.948Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).