Alkaid compiler cuts hardware latency for ultra-fast data pipelines
Alkaid: A Compiler Framework for Ultra-Low-Latency Kernels on Hardware
Hardware Architecture
Summary
Some computer programs need to work incredibly fast with tiny delays, especially in machine learning. Alkaid is a new free tool that helps turn parts of these programs into hardware designs quickly and efficiently, making sure they use less chip space and run faster. The creators designed it to understand detailed number representations and optimize the hardware’s performance without long waits. It works well on different types of machine learning tasks and even on other data tasks like sorting and counting.
What this means in practice
- •For hardware engineers: Create highly efficient hardware designs for machine learning models with faster turnaround and lower resource use.
- •For embedded systems developers: Integrate ultra-low-latency data processing kernels such as sorting or histograms into hardware-accelerated devices.
Authors
Chang Sun, Zhiqiang Que, Dimitrios Danopoulos, Maurizio Pierini, Wayne Luk, Maria Spiropulu
Abstract
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.