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SVRF: Efficient Register Storage for Long-Vector Architectures
Hardware Architecture · Submitted 5 Oct 2026
abstract compiled · 2026-10-07 · no language-model review
The authors' main claim Across representative HPC and AI/ML workloads, the SVRF reduces vector-register pressure and VRF activity without degrading performance.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Vector processors exploit data-level parallelism to provide high computational throughput, but very long vectors can make the Vector Register File (VRF) a significant source of area, energy, and register pressure. This cost is exacerbated when vector instructions operate on values with scalar semantics: although such values are architecturally represented using vector registers, they do not require full-width storage. This paper proposes the Scalarized Vector Register File (SVRF), a microarchitectural enhancement that redirects scalar-semantics vector values to a dedicated scalar register file, avoiding unnecessary allocation and access of full-width physical vector registers. The SVRF integrates with vector register renaming while preserving compatibility with the supported vector ISA. We implement and evaluate SVRF in an Register Transfer Level (RTL)-based RISC-V Vector Extension (RVV)-compliant vector processing unit supporting very long vectors. Across representative HPC and AI/ML workloads, the SVRF reduces vector-register pressure and VRF activity without degrading performance. By exploiting the resulting reduction in VRF demand, the proposed design achieves up to 15.8% VRF area reduction and up to 6% total area reduction, while reducing total power by up to 12% in evaluated configurations. Some workloads also achieve up to 1.12X speedup.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Vector processors exploit data-level parallelism to provide high computational throughput, but very long vectors can make the Vector Register File (VRF) a significant source of area, energy, and register pressure. | background 1.00 | 0.07 | 0.03 | asserted 0.95 |
| s1 | This cost is exacerbated when vector instructions operate on values with scalar semantics: although such values are architecturally represented using vector registers, they do not require full-width storage. | background 0.99 | 0.09 | 0.03 | asserted 0.99 |
| s2 | This paper proposes the Scalarized Vector Register File (SVRF), a microarchitectural enhancement that redirects scalar-semantics vector values to a dedicated scalar register file, avoiding unnecessary allocation and access of full-width physical vector registers. | contribution 0.95 | 0.79 | 0.23 | asserted 1.00 |
| s3 | The SVRF integrates with vector register renaming while preserving compatibility with the supported vector ISA. | method 0.79 | 0.59 | 0.03 | asserted 1.00 |
| s4 | We implement and evaluate SVRF in an Register Transfer Level (RTL)-based RISC-V Vector Extension (RVV)-compliant vector processing unit supporting very long vectors. | method 0.80 | 0.96 | 0.03 | asserted 1.00 |
| s5 | Across representative HPC and AI/ML workloads, the SVRF reduces vector-register pressure and VRF activity without degrading performance. | result 1.00 | 0.95 | 0.46 | asserted 1.00 |
| s6 | By exploiting the resulting reduction in VRF demand, the proposed design achieves up to 15.8% VRF area reduction and up to 6% total area reduction, while reducing total power by up to 12% in evaluated configurations. | result 1.00 | 0.96 | 0.47 | asserted 0.98 |
| s7 | Some workloads also achieve up to 1.12X speedup. | result 1.00 | 0.93 | 0.10 | asserted 0.88 |
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The paper
Original abstract
Vector processors exploit data-level parallelism to provide high computational throughput, but very long vectors can make the Vector Register File (VRF) a significant source of area, energy, and register pressure. This cost is exacerbated when vector instructions operate on values with scalar semantics: although such values are architecturally represented using vector registers, they do not require full-width storage. This paper proposes the Scalarized Vector Register File (SVRF), a microarchitectural enhancement that redirects scalar-semantics vector values to a dedicated scalar register file, avoiding unnecessary allocation and access of full-width physical vector registers. The SVRF integrates with vector register renaming while preserving compatibility with the supported vector ISA. We implement and evaluate SVRF in an Register Transfer Level (RTL)-based RISC-V Vector Extension (RVV)-compliant vector processing unit supporting very long vectors. Across representative HPC and AI/ML workloads, the SVRF reduces vector-register pressure and VRF activity without degrading performance. By exploiting the resulting reduction in VRF demand, the proposed design achieves up to 15.8% VRF area reduction and up to 6% total area reduction, while reducing total power by up to 12% in evaluated configurations. Some workloads also achieve up to 1.12X speedup.
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