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SVRF: Efficient Register Storage for Long-Vector Architectures

Francesco Minervini, Lorenzo Deltetto, Osman Unsal, Adrian Cristal

arXiv:2610.07078v1cs.AR

TL;DR

Scalar-semantics vector instructions can require full-width VRF storage despite representing scalar values, a mismatch that becomes costly as vector lengths grow. The paper proposes an SVRF that stores these values separately within the VPU. Across the evaluated representative workloads, it reports lower VRF pressure and power, area reductions, and performance improvement without degradation.

  • Problem

    Scalar-semantics vector operations use full-width registers despite representing scalar values, making VRF storage, access energy, and renaming pressure costly as vector lengths grow.

    Source: §1 · p. 1, §2.2 · p. 3

  • Method

    The SVRF is a dedicated scalar storage structure integrated with vector register renaming to hold scalar-semantics vector values and distribute them to lanes on demand.

    Source: §1 · p. 2, §3.2 · p. 4

  • Results

    Across representative HPC and AI/ML workloads, SVRF reduces VRF pressure and activity; reported outcomes include up to 1.12X performance improvement, 12% total power reduction, and 15.8% VRF area reduction.

    Source: §1 · p. 2

  • Takeaways & Limitations

    The microarchitectural mechanism preserves vector-instruction semantics while avoiding full-width allocation for scalar-valued results and can apply independently of the supported vector ISA in processors with register renaming.

    Source: §1 · p. 2, §7 · p. 16

    The evaluated design space is limited to VLEN≥4096; lower VLEN values require more substantial microarchitectural changes and are left for future investigation.

    Source: §5.4 · p. 13

Abstract

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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.

1 Introduction

Scalar-semantics vector instructions still allocate full-width registers, making VRF storage and energy costly as vectors grow. The paper introduces the SVRF to store scalar values separately and reports improved efficiency across representative HPC and AI/ML workloads without performance degradation.

1 Introduction

  • Scalar-semantics instructions can replicate or produce a single scalar, yet modern vector ISAs allocate and access full-width registers for them.This mismatch increases VRF occupancy, physical register pressure, and dynamic energy as vector length grows.Source: §1 · p. 1
  • The SVRF stores broadcast and reduction scalar values separately and distributes them to vector lanes on demand, avoiding full-width VRF allocation while preserving ISA compliance.The proposed mechanism integrates with vector register renaming through lightweight allocation and mapping changes.Source: §1 · p. 2
  • Up to 1.12X performance improvement and 12% total power reduction are reported alongside up to 15.8% VRF area reduction.The evaluation covers representative HPC and AI/ML workloads and reports improved area and energy efficiency without affecting execution performance.Source: §1 · p. 2
  • The paper reports that the mechanism can be implemented in vector implementations equipped with register renaming, regardless of supported vector ISA.The work evaluates an RTL implementation through physical implementation.Source: §1 · p. 2

2 Background

Vector architectures use vector registers to exploit structured data-level parallelism, and scalable vector ISAs permit implementations with different physical vector lengths. As vectors grow, full-width VRF capacity, bandwidth, and renaming requirements create substantial area and energy challenges.

2.1 Evolution of Vector Architectures

  • Vector processing applies one instruction across long data streams, and its history includes systems such as the CDC STAR-100 and Cray-1.Deeply pipelined functional units, vector registers, memory interleaving, and vector chaining supported throughput and reduced memory-bandwidth pressure.Source: §2.1 · p. 2

2.2 Scalable Vector Architectures

  • RVV is a Vector Length Agnostic ISA: programs can run unmodified on RVV-compliant processors with different implementation-dependent VLEN values.RVV supports vector registers up to 65536 bits per register.Source: §2.2 · pp. 2–3
  • Scalar broadcasts, scalar-indexed gathers, and reductions have constant logical scalar footprints but conventionally occupy full-width vector registers, so their storage cost scales with vector length.This mismatch is increasingly costly when vector registers span multiple kilobytes.Source: §2.2 · p. 3

2.3 Vector Register File Design Challenges

  • As vector length increases, wider VRF storage and multiport bandwidth raise capacity, power, routing, area, and timing challenges.Banking can reduce port pressure but introduces conflict-management and layout trade-offs.Source: §2.3 · p. 3
  • Register renaming requires enough physical vector registers to expose instruction-level parallelism and avoid WAW and WAR hazards, but more registers increase storage and energy costs.The NEC VE20 example has 256 physical vector registers and 512 kB of VRF storage in one core.Source: §2.3 · p. 3
  • Prior VRF approaches optimize organization, banking, or access cost, while generally retaining full-width allocation for every vector result.The passage identifies reducing unnecessary storage allocation as increasingly relevant as vector lengths grow.Source: §2.3 · p. 3

3 Scalar-Aware Vector Register Architecture

The architecture adds an SVRF to hold scalar-valued vector results separately from full-width vector registers while preserving the architectural execution model. Renaming maps scalar results to scalar storage when available, and execution paths either write directly at rename or redirect computed results to the SVRF.

3 Scalar-Aware Vector Register Architecture

  • The SVRF decouples scalar-valued vector operands from the full-width VRF, reducing VRF pressure without changing the architectural vector execution model.It is a lightweight scalar storage structure within the VPU and remains invisible to software.Source: §3 · p. 3, §3.2 · pp. 4–5

3.1 Baseline Vector Microarchitecture

  • In the baseline design, the VPU’s renaming subsystem maintains up to 64 physical vector registers, and all vector instructions—including reductions, scalar broadcasts, and scalar-indexed gathers—allocate full-width physical vector registers regardless of scalar semantics.Each architectural vector register holds up to 2 kB of data (VLEN=16384 bits).Source: §3.1 · p. 4

3.2 Design Overview

  • The design separates scalar-semantics instructions into rename-resolved broadcasts and execution-resolved reductions or scalar-indexed gathers.The SVRF is kept within the VPU rather than reusing the scalar core register file, preserving renaming independence and execution locality.Source: §3.2 · pp. 4–5

3.3 Register Renaming Extensions

  • The FSRL tracks available SVRF entries; when none are free, scalar-semantics instructions can instead use an available physical vector register so the front end does not stall.The RMT marks whether each architectural vector register maps to scalar or vector physical storage.Source: §3.3 · p. 5

3.4 Scalarized Vector Register File

  • The SVRF is a configurable multiported register file with 64-bit entries, two write ports, and five read ports for scalar-consuming accesses issued per cycle.Each entry has a valid bit; a requested value is returned in the next cycle when valid.Source: §3.4 · pp. 5–6

3.5 Scalar-semantics Instruction Execution

  • Rename-resolved instructions write scalar values directly to the SVRF and complete without vector-lane execution; execution-resolved results are redirected there at writeback.SVRF-mapped source operands are read at issue and distributed to lanes, consuming no additional VRF bandwidth.Source: §3.5 · pp. 6–7

4 Methodology

The evaluation uses an RTL implementation of the SVRF and related renaming and execution mechanisms. The design is configurable, and its SVRF reads take one cycle without adding latency to lane handshakes.

4 Methodology

  • The evaluation methodology covers the RTL implementation, functional-validation platforms, benchmarks, and assessment metrics.The methodology describes evaluation through the physical implementation stage.Source: §4 · p. 7

4.1 RTL Implementation

  • The SVRF and related mechanisms in renaming and execution stages are implemented in SystemVerilog RTL.Its register array uses a configurable flip-flop-based memory model whose architectural parameters can be changed at instantiation.Source: §4.1 · p. 7
  • SVRF reads take one clock cycle but occur in parallel with validation of other head-instruction operands, adding no latency to handshakes with vector lanes.The additional renaming structures do not add front-end latency or reduce the baseline renaming throughput.Source: §4.1 · p. 7

4.2 Experimental Setup

The VPU is validated in separate standalone and system-level UVM environments. Standalone tests stress the VPU architecture, while system-level tests exercise its integration with a scalar core and memory model.

4.2 Experimental Setup

  • The UVM environments support the evaluation metrics, and experiments run with Siemens Questa Sim 2024.3.The setup covers both functional validation and system-level evaluation.Source: §4.2 · p. 7

4.2.1 Standalone VPU Simulation

  • Standalone UVM tests stress the VPU with RVV-representative synthetic tests and more than 30,000 pseudo-random vector instructions.The VPU is the device under test, with the environment idealizing the rest of the system to focus on VPU bottlenecks.Source: §4.2.1 · p. 7

4.2.2 System-Level Simulation

  • System-level UVM validation integrates the VPU with an in-house out-of-order scalar core, L1 instruction and data caches, and a main-memory model.The scalar core is an unmodified black-box component, and the interface follows the original OVI specifications [34].Source: §4.2.2 · p. 7

4.3 Evaluated Configurations.

The evaluation varies vector-register count, SVRF scalar-register count, and vector length. It combines one-at-a-time sweeps with configurations that test performance-preserving reductions in vector-register capacity.

4.3 Evaluated Configurations.

  • Table 1 summarizes configurations spanning physical vector-register count, SVRF scalar-register count, and VLEN.The study varies one microarchitectural element at a time, then combines settings to characterize the mechanism across target workloads.Source: §4.3 · p. 8, Table 1 · p. 8
  • For VLEN=8192 and VLEN=4096, the design uses 56 and 48 physical vector registers, respectively, after scaling VLEN while maintaining throughput and aggregate VRF-to-functional-unit bandwidth.For each VLEN, the minimum vector-register count that preserves performance is determined; the study then varies SVRF scalar-register count for performance and area.Source: §4.3 · p. 8
  • The VLEN16k-V64-S8 label denotes the baseline VLEN=16384-bit architecture configured with eight physical SVRF scalar registers.The evaluated VLEN8k and VLEN4k configuration families are also summarized in Table I.Source: §4.3 · p. 8

4.4 Physical Implementation and Power Estimation

The configurations are synthesized to estimate area and power, with workload-driven gate-level switching activity used for the power analysis.

4.4 Physical Implementation and Power Estimation

  • Design Compiler synthesizes the configurations for TSMC N4C 4 nm, with clock-gated cells enabled and VRFs generated as register-file macros.The VRF macros come from the technology PDK memory-compiler portfolio.Source: §4.4 · p. 8
  • Gate-level simulations use back-annotated timing and workload-representative instruction streams to capture switching activity for PrimePower.The UVM verification environments drive the simulations.Source: §4.4 · p. 8
  • PrimePower estimates total power from workload-derived switching activity and synthesized-design timing; area comes from Design Compiler synthesis reports.Unless otherwise stated, power is the VPU's average total power during workload execution.Source: §4.4 · pp. 8–9

4.5 Workloads

The evaluation uses vectorized kernels from RVV benchmark and vector-architecture software, with representative application patterns adapted for RTL simulation. The workloads include HPC and AI/ML computations and exercise scalar-vector moves and reductions.

4.5 Workloads

  • RTL evaluation uses cycle-accurate simulation of representative application regions, adapted as bare-metal standalone programs while preserving core computation patterns.The setup avoids operating-system and runtime-library dependencies because RTL simulation of large designs is computationally costly.Source: §4.5 · p. 9
  • Other kernels cover particle filtering, layer normalization, 3D particle potential and relocation, Black-Scholes option pricing, and Monte Carlo swaptions pricing.These workloads are particle filter, layernorm, lavaMD, blackscholes, and swaptions.Source: §4.5 · p. 9
  • The evaluated kernels include deformable mass-spring dynamics, scaled vector addition, matrix multiplication, and element-wise products followed by vector reductions.These are somier, axpy, gemm, and dotproduct, respectively.Source: §4.5 · p. 9
  • Table 2 lists the evaluation kernels and input sizes; workloads derive from RiVEC and published vector-architecture software for HPC and AI/ML applications.The kernels are compiled with the RISC-V GNU toolchain and manually checked for simulation compatibility.Source: Table 2 · p. 9, §4.5 · p. 9

4.6 Metrics

The evaluation measures how redirecting scalar-semantics results to the SVRF affects execution time, area, and power. These metrics assess resource use, hardware cost, and workload execution.

4.6 Metrics

  • Performance is measured by comparing execution time against the baseline VPU across benchmarks and varying physical scalar and vector register counts.The reported impact depends on the frequency and instruction-stream position of scalar-semantics instructions.Source: §4.6 · p. 10
  • Area is measured from synthesis reports to assess whether reduced vector-register use enables a smaller physical VRF without compromising performance.The comparison evaluates configurations that trade vector-register allocation for scalar-register allocation.Source: §4.6 · p. 10
  • Power is estimated using gate-level simulation to assess dynamic savings from fewer VRF accesses and static savings from smaller VRF configurations.The passage identifies these as potential sources of power benefit.Source: §4.6 · p. 10

5 Evaluation

The evaluation tests SVRF configurations against a conventional-VRF baseline across workloads and hardware configurations. It reports preserved or improved performance alongside area and power savings, with shorter-vector results also examined.

5.1 Performance Impact

  • Most workloads retain execution times comparable to the VLEN16k-V64 baseline; particle filter and blackscholes reach 1.10X and 1.12X speedup with VLEN16k-V64-S16.The reduced-register VLEN16k-V48-S16 and VLEN16k-V40-S16 configurations also outperform the baseline on particle filter, blackscholes, and swaptions.Source: §5.1 · pp. 10–11

5.3 Power consumption

  • 8.4% lower dynamic power is reported for blackscholes with VLEN16k-V64-S16; static power falls by up to 13% for VLEN16k-V40-S16 and 1.3% for VLEN16k-V48-S8.Dynamic savings include eliminating VRF writes for rename-resolved scalar results and bypassing VRF reads when retrieving values from the SVRF.Source: §5.3 · pp. 11–12, Figure 7 · p. 14

5.2 Hardware Cost and Area Impact

  • 15.8% lower VRF area is reported for VLEN16k-V40-S16, while total area falls by up to 6% versus baseline.The SVRF adds 1.52% area in the VLEN16k-V64-S16 configuration before VRF reduction; recovered area could support one additional FPU per vector lane.Source: §5.2 · p. 11, Figure 5 · p. 13

5.4 Impact of Vector Length

  • For VLEN=8192 and VLEN=4096, reduced-register SVRF configurations maintain comparable performance; VLEN8k-V40-S16 reduces VRF area by up to 7.2%.The exploration covers VLEN≥4096; lower lengths are outside this implementation's design space and would require more substantial microarchitectural changes.Source: §5.4 · pp. 13–14

6 Related Work

Prior VRF work improves storage organization, caching, or access efficiency, but continues to place vector results in full-width registers. The paper identifies scalar-semantics results as a distinct inefficiency addressed by SVRF.

6 Related Work

  • AVA maps short vectors to the VRF and longer vectors across the VRF and a reserved memory-VRF area, swapping needed registers into the VRF.Titopoulos et al. cache recently accessed vector registers, while MANIC forwards intermediate values between functional units.Source: §6 · p. 14
  • Ara2 distributes its VRF across vector lanes, while AraXL explores physically scalable implementations; both focus on scalable datapaths and efficient VRF implementation, yet store all results in full-width vector registers.Source: §6 · p. 15
  • SVRF targets scalar-valued vector results, separating them from the full-width VRF; the authors state that prior work had not explored this separation.The extension is transparent to the ISA and applies to vector processors with register renaming, independently of the specific vector ISA.Source: §6 · p. 15

7 Conclusion

SVRF stores scalar-valued vector results in a dedicated scalar structure and distributes them to vector lanes on demand, avoiding full-width vector-register allocation while preserving instruction semantics. The microarchitectural mechanism can integrate with vector register renaming independently of the supported vector ISA.

7 Conclusion

  • SVRF avoids allocating full-width vector registers for scalar-valued results by storing them in a dedicated scalar structure and distributing them to lanes on demand.The conclusion identifies exploring further scalarization opportunities and evaluation across different vector lengths, including embedded designs, as future work.Source: §7 · p. 16
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