A compiler platform for verified structured sparsity
Maple Silicon™ is an early-stage compiler platform being developed by Maple Silicon Inc. The project focuses on making structured N:M sparsity a verifiable, end-to-end property in modern machine learning compilation pipelines.
Rather than relying on backend-specific kernel optimizations, Maple Silicon approaches sparsity as a compiler responsibility — preserving intent, verifying constraints, and enabling safe lowering to sparse execution paths only when correctness can be guaranteed.
Maple Silicon is currently in active development.
At this stage, the work is focused on building and validating the compiler foundations required to support structured sparsity safely and correctly. This includes:
The current emphasis is on correctness, reproducibility, and architectural soundness, rather than headline performance numbers.
Many modern machine learning models exhibit structured sparsity, such as N:M patterns, either through training techniques or hardware-driven constraints.
However, most existing software stacks treat sparsity as a backend optimization problem. This often leads to fragile implementations, limited portability, and difficulty validating whether sparse execution is actually correct or applicable in a given context.
As a result, structurally sparse models are frequently executed as dense workloads, leaving potential efficiency gains unrealized.
Maple Silicon treats structured sparsity as a first-class compiler concern.
The platform is designed around the following principles:
Structured N:M sparsity patterns are represented explicitly in the compiler IR, making sparsity intent visible throughout the compilation pipeline.
All required constraints are verified before any sparse transformation, ensuring that optimizations are only applied when safety can be guaranteed.
Dense operations are rewritten to sparse equivalents only when verification passes, maintaining correctness as the primary goal.
A fallback path to dense execution is always available when verification fails, ensuring no correctness compromises.
This approach prioritizes correctness and transparency, making sparsity behavior predictable, auditable, and easier to reason about across different targets.
The current technical scope of Maple Silicon includes:
Maple Silicon is not a hardware-specific solution. The goal is to provide a portable, compiler-level foundation that can integrate with multiple runtime and backend environments.
Maple Silicon is currently an early-stage research and development effort.
At this stage, the project does not claim:
These areas are intentionally being approached incrementally, following correctness validation and reproducible benchmarking.
The next phase of Maple Silicon development focuses on validation and expansion:
These steps are aimed at building confidence in the architecture before pursuing broader adoption or performance claims.
Maple Silicon Inc. is a Canadian technology company focused on compiler infrastructure and systems-level optimization for machine learning and high-performance computing workloads.
Maple Silicon™ is the company's initial platform, developed as part of ongoing research and engineering efforts into structured sparsity and efficient execution.
Maple Silicon Inc. is open to research collaboration, pilot evaluations, and discussions related to Canadian innovation and funding programs, including NRC IRAP.
Email: info@maplesilicon.co