Sparse-ternary-fma – 23x faster AVX-512 kernel for FHE&AI, from my ePrint paper (github.com)

The recent introduction of the sparse-ternary-fma kernel marks a significant leap in the performance of Fully Homomorphic Encryption (FHE) applications, particularly in the context of AI and machine learning. Traditional multiplicative processes in FHE are computationally intensive, often leading to inefficiencies with memory usage when using ternary secret keys. The sparse-ternary-fma kernel addresses this by utilizing a compact 2-bit representation for ternary values, drastically improving data density and reducing memory consumption. Additionally, it maximizes efficiency through sparse processing, targeting non-zero elements for faster calculations, and harnesses SIMD technology for parallelized operations. This innovative approach yields remarkable performance metrics, boasting a 2.38x increase in throughput and up to 26.12x reduction in latency compared to previous implementations. By open-sourcing this kernel under the MIT license, the developers aim to foster community collaboration, paving the way for advancements in FHE and low-precision AI applications. This kernel not only accelerates client-side FHE but also lays a foundation for further developments in secure computing, thus potentially reshaping how AI systems manage and process sensitive data.