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Alkaid: A Compiler Framework for Ultra-Low-Latency Kernels on Hardware

Chang Sun, Zhiqiang Que, Dimitrios Danopoulos, Maurizio Pierini, Wayne Luk, Maria Spiropulu

arXiv:2610.04808v1cs.ARhep-ex

TL;DR

Sub-microsecond static dataflow kernels need fine-grained bitwidth control and arithmetic optimization, while layer-template ML-to-hardware tools are less flexible for new operators and custom processing logic. Alkaid translates Python/NumPy descriptions and ML frontend models through an arithmetic-centric IR that preserves fixed-point semantics and heterogeneous bitwidths, with hardware-aware optimization and white-box performance estimation. Alkaid generally achieves 20–30% lower LUT usage than da4ml or hls4ml with the da4ml plugin, and up to 40% lower latency on some designs versus original designs.

  • Problem

    Sub-microsecond static dataflow kernels need fine-grained bitwidth control and arithmetic optimization, while layer-template ML-to-hardware tools are less flexible for new operators and custom processing logic.

    Source: §I · p. 1

  • Method

    Alkaid translates Python/NumPy descriptions and ML frontend models through an arithmetic-centric IR that preserves fixed-point semantics and heterogeneous bitwidths, with hardware-aware optimization and white-box performance estimation.

    Source: §I · p. 1

  • Results

    Alkaid generally achieves 20–30% lower LUT usage than da4ml or hls4ml with the da4ml plugin, and up to 40% lower latency on some designs versus original designs.

    Source: B. Performance and Resource Usage · p. 6

  • Takeaways & Limitations

    Alkaid supports static dataflow workloads beyond neural networks, including sorting and histogram kernels, while generating stateless pipelined designs.

    Source: §V · p. 8

    The pre-routing performance model cannot capture variation from target clock frequency, routing congestion, or synthesis-tool version.

    Source: A. Performance Modeling · p. 5

Abstract

from arXiv · show

Ultra-low-latency machine learning and data processing pipelines operating on the sub-microsecond level often contain static dataflow kernels that require fine-grained bitwidth control, arithmetic optimization, and fast hardware performance estimation. This works introduce Alkaid, a free and open source domain specific compiler that translates sub-microsecond latency dataflow kernels into platform agnostic Register Transfer Level (RTL) or High-Level Synthesis (HLS)-ready code in seconds. Alkaid targets ultra-low-latency ML pipelines, especially those addressed by existing ML-to-hardware flows such as \texttt{hls4ml}, while also supporting the surrounding pre-processing, post-processing, and control logic. At its core, Alkaid uses an arithmetic centric intermediate representation, Alkaid Low-level IR (ALIR), to preserve fixed-point semantics, heterogeneous bitwidths, and operation level structure for optimization and code generation. Alkaid further provides comprehensive hardware-aware optimization passes and an interpretable white-box analytical performance model for rapid design space exploration without synthesis in the loop. Across representative ML workloads, including MLPs, GNNs, and transformers, Alkaid achieves 20-30\% lower LUT usage than the best prior flows for bit-exact equivalent designs, with up to 40\% lower latency in some cases. Beyond typical neural networks, Alkaid achieves comparable performance to the state-of-the-art method for synthesizing boosted decision trees, while also enabling the implementation and integration of non-ML kernels such as sorting networks and histograms.

I. INTRODUCTION

Existing ML-to-hardware tools rely on coarse-grained layer templates, limiting flexibility for new operators and kernels with custom processing logic. Alkaid introduces an arithmetic-centric compiler framework and reports lower resource use and latency for bit-exact neural-network designs.

I. INTRODUCTION

  • Existing frameworks such as hls4ml and FINN use coarse-grained layer templates, making new operators and custom pre/post-processing less flexible.The cited examples include tensor arrangement, selection, sorting, and control logic.Source: §I · p. 1
  • The introduction characterizes Alkaid as the best-performing sub-microsecond dataflow compiler framework and the first to provide accurate white-box analytical performance modeling.The comparison defines hardware-aware optimization as explicit transformations beyond pragma tuning and basic algebraic simplifications.Source: §I · pp. 1–2
  • Alkaid translates Python/NumPy descriptions and ML frontend models into platform-agnostic RTL or HLS-ready code for ultra-low-latency ML and surrounding data-processing kernels.Its stated scope includes pre- and post-processing and control logic.Source: §I · p. 1
  • ALIR preserves fixed-point semantics, heterogeneous bitwidths, and operation-level structure across tracing, optimization, performance modeling, and code generation.The compiler also provides hardware-aware optimization and a white-box model for area and latency estimation without synthesis in the loop.Source: §I · p. 1
  • 20–30% lower LUT usage and up to 40% lower latency for bit-exact neural-network designs versus da4ml or hls4ml+da4ml, with compilation and synthesis time reduced from hours or days to under an hour.The evaluation also covers MLPs, GNNs, transformers, boosted decision trees, sorting networks, histograms, and custom Python/NumPy kernels.Source: §I · pp. 1–2

II. BACKGROUND AND RELATED WORK

Prior FPGA frameworks target neural-network layers or other latency regimes, while Alkaid focuses on static dataflow kernels. Its Python/NumPy tracing frontend builds an IR that can be transformed and lowered to RTL or HLS code.

II. BACKGROUND AND RELATED WORK

  • Neural-network FPGA frameworks such as hls4ml, chisel4ml, FINN, and fpgaConvNet use coarse-grained templates, making new layers difficult to implement.TreeLUT, Conifer, and FwX, address decision trees and tree ensembles.Source: §II · p. 2
  • General compiler and HLS frameworks are not directly comparable because their target applications have latencies orders of magnitude higher than Alkaid’s sub-microsecond regime.The passage identifies hls4ml and FINN as dominant frameworks in that regime.Source: §II · p. 2

III. DESIGN AND ARCHITECTURE

  • Alkaid targets static dataflow kernels whose computation-graph structure and data types are determined at compile time, using Python/NumPy symbolic execution to trace operations into an IR.Users replay the kernel on a symbolic tensor using native arithmetic and NumPy operations.Source: §III · p. 2
  • After IR generation, users can apply built-in or custom transformations, then generate RTL or HLS code for synthesis or further manual optimization.The architecture is illustrated in Figure 1.Source: §III · p. 2

1) ALIR:

ALIR is an arithmetic-centric static-dataflow IR that preserves fixed-point arithmetic and heterogeneous bitwidths for low-latency hardware optimization. Alkaid combines tracing-time and global IR optimizations, with user-extensible passes.

1) ALIR:

  • ALIR keeps fixed-point operations and heterogeneous bitwidths implicit in an arithmetic-centric SSA representation, supporting bitwidth optimization and direct hardware code generation.Its operation semantics handle bit extensions and concatenations without explicit bit-manipulation instructions.Source: 1) ALIR · p. 2

2) Optimizations:

  • For kernels sized to fit one FPGA (about 1M LUTs), tracing, IR generation, and optimization take milliseconds to under a minute.Users can enable or disable individual optimizations and define custom passes.Source: 2) Optimizations · pp. 2–3
  • The da4ml+ reimplementation uses optimized C++ memory access patterns and modified tie-breaking, running 2.5–3 times faster than da4ml with the same or better adder counts under the same logic-depth constraint.Examples include 13.3 to 5.5 ms for 32×32×8-bit matrices and 84 to 24 s for 128×128×8-bit random matrices.Source: 2) Optimizations · p. 3
  • Alkaid applies local and past-operation-dependent optimizations during symbolic tracing, then iteratively runs global optimizations after IR construction.Global passes include canonicalization, common sub-expression elimination, dead/null operation elimination, and further constant folding.Source: 2) Optimizations · p. 3

B. Performance Model

Alkaid estimates area and latency from ALIR operation structure using an interpretable analytical model. Its estimates omit fractional LUT usage for LOOKUP operations.

B. Performance Model

  • The white-box model combines first-principles analysis and microbenchmark fitting to estimate each ALIR operation’s LUT cost and combinational delay without synthesis.Evaluation typically takes under a second per kernel.Source: B. Performance Model · p. 3
  • Table II lists surrogate resource and latency models for ALIR operations, while the model estimates area using LUT consumption alone.DSP usage is negligible after distributed-arithmetic optimization, and FF usage depends mainly on pipelining.Source: B. Performance Model · p. 3, Table II · p. 4
  • The LOOKUP estimator excludes fractional LUT usage because LUT6_2 fracturing is rarely used for LOOKUP.Source: B. Performance Model · p. 4

C. Symbolic Tracing Frontend

Alkaid’s symbolic tracing frontend defines kernels with native Python and NumPy operations on symbolic tensors, tracking bit growth while preserving bit-exact hardware behavior. Users must explicitly quantize operations and weights.

C. Symbolic Tracing Frontend

  • Symbolic tensor operations match hardware operations, with intermediate bit growth tracked using affine arithmetic when possible and interval arithmetic otherwise.Source: C. Symbolic Tracing Frontend · p. 4
  • The frontend applies no implicit quantization, so users must supply properly quantized operations and weights or generated firmware may become unsynthesizable from excessive intermediate values.Keras and PyTorch frontends trace most native operations, while plugins map special layers or operators to symbolic operations.Source: C. Symbolic Tracing Frontend · p. 4
  • The plugin system lets frontends evolve independently and represent framework-specific semantics, including padding and quantized lookup behavior, through different symbolic mappings.Source: C. Symbolic Tracing Frontend · p. 4

1) Layer/Operation Plugin:

Alkaid supports frontend plugins, multiple code-generation targets, and integration with hls4ml. Explicit quantization is required for models entering through the symbolic tracing frontend.

1) Layer/Operation Plugin:

  • Alkaid supports Keras and PyTorch elementary operations, but models must be explicitly quantized before use with symbolic tracing.HGQ, PQuantML, and torchlogix plugins are implemented with the Keras and Torch frontends.Source: 1) Layer/Operation Plugin · p. 4

2) Framework-wise Plugin:

  • Framework-wise plugins map external models into symbolic tensors; XGBoost users can configure leaf-value quantization and lookup-table versus mux implementation.The hls4ml plugin converts optimized Vitis HLS-targeted models into ALIR for further lowering while remaining bit-exact to the original model.Source: 2) Framework-wise Plugin · p. 4

D. Code Generation Backend

  • Alkaid generates platform-agnostic RTL, Vitis HLS and oneAPI DPC++ code, and XLS IR; additional targets require a code generator and corresponding performance-model adjustments.Source: D. Code Generation Backend · p. 4
  • Figure 2 compares estimated and post-route LUT usage and latency, with larger JEDI-net designs showing higher prediction errors as routing congestion increases.Source: Figure 2 · p. 5

E. hls4ml Integration

  • With the hls4ml/Keras frontend, unsupported layers are converted to ALIR and Alkaid-generated HLS code is inlined into the hls4ml output for synthesis.Source: E. hls4ml Integration · p. 5

IV. EVALUATION

Alkaid produces bit-exact neural-network RTL with lower LUT usage and latency than da4ml or hls4ml+da4ml, and achieves efficiency comparable to TreeLUT for decision-tree models.

IV. EVALUATION

  • Unless otherwise stated, results use Vivado 2025.1 on an UltraScale+ VU13P FPGA and report latency in cycles at maximum achievable clock frequency.The reports are out-of-context post-routing; designs are registered at both input and output ports.Source: §IV · p. 5
  • Alkaid’s bit-exact neural-network designs use 20–30% less LUTs and up to 40% less latency than da4ml or hls4ml+da4ml implementations.The comparisons cover all evaluated neural-network designs.Source: §IV · p. 5
  • Against standalone hls4ml, Alkaid achieves 50–70% LUT reduction and >99% DSP reduction without da4ml algorithm optimization.Source: §IV · p. 5
  • For decision-tree-based models, Alkaid achieves LUT usage and latency comparable to TreeLUT.Source: §IV · p. 5

A. Performance Modeling

Alkaid’s performance model is evaluated against Vivado post-routing results across machine-learning models and sorting networks. It generally estimates LUT usage within 10% and latency within 20%, while routing and tool factors remain outside its scope.

A. Performance Modeling

  • Evaluation covers 2,410 designs, including MLPs, static GNNs, JEDI-linear, TGCNN, transformers, and parallel sorting networks.The designs span LUT usage from about 500 to over 500,000.Source: A. Performance Modeling · p. 5
  • The model generally estimates LUT usage within <10% and latency within <20% of Vivado 2025.1 post-routing results.Target clock frequency, routing congestion, and synthesis-tool version can cause larger variation that the model cannot capture.Source: A. Performance Modeling · p. 5
  • The authors consider the estimation accuracy sufficient for rapid design iteration and design-space exploration before synthesis.Source: A. Performance Modeling · p. 5

B. Performance and Resource Usage

Alkaid reduces LUT usage across neural-network comparisons, with latency gains on some designs, and achieves similar resource use and latency to TreeLUT for boosted decision trees. Its model-to-RTL conversion and synthesis cycle is also faster than the compared HLS-based flows.

B. Performance and Resource Usage

  • 20–30% lower LUT usage versus da4ml or hls4ml+da4ml, with up to 40% lower latency on some bit-exact neural designs.The HLF and PLF table captions report 20–30% LUT reductions, while the neural-network results describe latency reductions on some designs and similar latency on others.Source: B. Performance and Resource Usage · pp. 5–6, Table III · p. 6, Table IV · p. 6
  • Because exact accuracy-matched TreeLUT models were unavailable, the BDT comparison uses fitted trees and adjusts TreeLUT latency for protocol differences.The fitted models target the same accuracy; their accuracy is considered close enough for a meaningful resource and performance comparison.Source: B. Performance and Resource Usage · p. 6
  • For BDTs, Alkaid achieves similar resource usage and latency to TreeLUT at equivalent or slightly higher accuracy.The reported comparison adjusts TreeLUT latency for differences in evaluation protocols.Source: Table V · p. 7, B. Performance and Resource Usage · p. 7
  • JEDInet designs use 50–70% fewer LUTs and over 99% fewer DSPs in general, while achieving II=1 versus hls4ml’s II=2.CMVM optimization was disabled to match hls4ml’s accumulator overflow behavior; the designs used only H(k)+ODP SCM optimization.Source: B. Performance and Resource Usage · p. 7
  • Alkaid converts the evaluated models to RTL in under a minute, while each logic synthesis takes no more than 50 minutes.The compared hls4ml flows can require hours to days for a single HLS synthesis.Source: B. Performance and Resource Usage · p. 7

C. Non-Machine Learning Kernels

Alkaid converts non-ML Python/NumPy kernels, including sorting networks and histograms, into RTL. These kernels can be integrated with ML models and pre- or post-processing in one dataflow graph and RTL project.

C. Non-Machine Learning Kernels

  • Alkaid converts sorting-network and histogram kernels expressed in Python/NumPy into RTL, with latency and resource usage reported for these kernels.The examples use np.sort or np.argsort for sorting and np.histogram for histograms.Source: C. Non-Machine Learning Kernels · p. 7
  • Sorting and histogram kernels used in LHC preprocessing can share a dataflow graph with ML models and be converted into one RTL project.The cited uses include jet-constituent ordering and feature extraction or creation for downstream models.Source: C. Non-Machine Learning Kernels · p. 7

V. CONCLUSION

Alkaid combines a Python/NumPy tracing frontend, ALIR, and analytical performance modeling to generate RTL or HLS-ready code. It reports lower resource use and latency across neural-network workloads and supports non-ML kernels, with stateless pipelined designs as its current design choice.

V. CONCLUSION

  • Alkaid translates low-latency kernels into platform-agnostic RTL or HLS code using Python/NumPy symbolic tracing, ALIR, and white-box analytical modeling for rapid design-space exploration.Source: §V · p. 7
  • MLPs and transformers use 20-30% fewer LUTs and achieve up to 40% lower latency than da4ml or hls4ml+da4ml; GNNs use 50-70% fewer LUTs and >99% fewer DSPs than hls4ml standalone.For GNNs, end-to-end compilation and synthesis time falls from hours or days to under an hour.Source: §V · p. 8
  • Alkaid supports non-ML kernels such as sorting networks and histograms; its generated sorting, histogram, and custom Python kernels target an Altera Agilex 7 FPGA.Source: §V · p. 8, Table VI · p. 8
  • Alkaid currently generates stateless pipelined designs, treats scheduling and orchestration as higher-level steps, and identifies processing-element reuse and more complex graphs as future directions.The stated future direction also includes throughput-area trade-offs for larger models.Source: §V · p. 8
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