Accelerating Sparse Ternary GEMM for Quantized ML on Apple Silicon

• URL: http://arxiv.org/abs/2510.06957v2
• Date: Mon, 13 Oct 2025 15:54:30 GMT
• Title: Accelerating Sparse Ternary GEMM for Quantized ML on Apple Silicon
• Authors: Baraq Lipshitz, Alessio Melone, Charalampos Maraziaris, Muhammed Bilal,
• Abstract summary: We present a Sparse Ternary GEMM kernel optimized specifically for Apple's M-series processors.<n>We propose a set of architecture-aware optimizations, including a novel blocked and interleaved sparse data format to improve memory locality.<n>Our vectorized implementation delivers up to a 5.59x performance increase for large matrices with 25% sparsity, and remains stable across varying sparsity levels.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse Ternary General Matrix-Matrix Multiplication (GEMM) remains under-optimized in existing libraries for Apple Silicon CPUs. We present a Sparse Ternary GEMM kernel optimized specifically for Apple's M-series processors. We propose a set of architecture-aware optimizations, including a novel blocked and interleaved sparse data format to improve memory locality, strategies to increase Instruction-Level Parallelism (ILP), and NEON-based Single Instruction Multiple Data (SIMD) vectorization to exploit data-level parallelism. Our scalar implementation achieves up to a 5.98x performance increase over a traditional Ternary Compressed Sparse Column (TCSC) baseline for large matrices with 50% ternary nonzero values (sparsity), reaching up to a 50.2% of the processor's theoretical peak performance, and remains stable across varying sparsity levels. Our vectorized implementation delivers up to a 5.59x performance increase for large matrices with 25% sparsity, and remains stable across varying sparsity levels.

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