Wenguang Chen

Linear algebra computations can be greatly accelerated using spatial accelerators on FPGAs. As a standard building block of linear algebra applications, BLAS covers a wide range of compute patterns that vary vastly in data reuse, bottleneck resources, matrix storage layouts, and data types. However, existing implementations of BLAS routines on FPGAs are stuck in the dilemma of productivity and performance. They either require extensive human effort or fail to leverage the properties of routines for acceleration. We introduce Lasa, a framework composed of a programming model and a compiler, designed to address the dilemma by abstracting (for productivity) and specializing (for performance) the architecture of a spatial accelerator. The programming model realizes systolic arrays using uniform recurrence equations and space-time transforms. Streaming tensors, an intuitive dataflow-style abstraction, is proposed to uniformly describe the movement, storage, and transpose of input and output data across the spatial components. According to streaming tensors, a customized memory hierarchy is automatically built on an FPGA by our compiler. The compiler further specializes the architecture with transparent optimizations on FPGAs. Using this framework, we develop a complete BLAS library, demonstrating performance in parity with expert-written HLS code for BLAS level 3 routines, 76%–94% machine peak for level 1 and 2 routines, and 1.6X–13X speedup by leveraging the matrix properties such as symmetry, triangularity, and bandness.

The RNS-CKKS scheme in Fully Homomorphic Encryption (FHE) supports crucial features for privacy-preserving machine learning, such as fixed-point arithmetic and SIMD-style vectorization. Yet, managing the escalation of ciphertext scales from homomorphic multiplications, which risks capacity overflow, along with bootstrapping, presents significant challenges. These complexities are exacerbated by the need to efficiently handle scale and bootstrapping at compile time while ensuring rapid encrypted inference. In this paper, we present ReSBM, a novel compiler technique that simultaneously optimizes scale and bootstrapping for encrypted inference under RNS-CKKS. By partitioning a program's data flow graph (DFG) into regions with a uniform multiplicative depth of one, RESBM ensures that placements of Scale Management Operations (SMOs) and bootstraps affect only the latency of a region, not the scales and levels of its live-out ciphertexts. Our region-based approach tackles the NP-hard challenge of optimal bootstrapping placement with hierarchical strategies: (1) optimal intra-region SMO and bootstrapping placement using min-cut, (2) bootstrapping-guided rescaling region identification across a sequence of regions, culminating in tentative bootstrapping at two terminal regions, and (3) minimal-level bootstrap placement across the DFG, elevating ciphertexts only to the necessary minimal level. Validation across a variety of complex models on CPUs shows that ReSBM not only compiles these models more rapidly than a leading method but also boosts encrypted inference efficiency by an average of 12.1% when compared to another leading method. Consequently, ReSBM substantially improves the practical deployment of large models for encrypted inference, surpassing existing methods in terms of both compilation speed and inference performance.

Context-sensitive pointer analysis tends to generate excessive spurious points-to relations, causing inefficiency and imprecision. We introduce stack filtering, a novel approach using Rust's stack object lifetime information to address this issue. It identifies and eliminates context-sensitive points-to relations involving variables pointing to inactive stack objects beyond their lifetimes. Stack filtering is a lightweight two-phase process. It first assesses stack object liveness based on function reachability, leveraging an efficient call graph generated by Rapid Type Analysis (RTA). Then, this filtering is applied during the main pointer analysis. We have integrated stack filtering into Rᴜᴘᴛᴀ, a context-sensitive pointer analysis framework for Rust. Evaluations on real-world Rust applications show that stack filtering enhances analysis efficiency, reducing time and memory consumption while improving precision. This enhanced context-sensitive pointer analysis with stack filtering is poised to become a vital tool for Rust's program analysis and verification, given Rust's reliance on automatic memory management and a preference for static stack allocation.

Fully Homomorphic Encryption (FHE) facilitates computations on encrypted data without requiring access to the decryption key, offering substantial privacy benefits for deploying neural network applications in sensitive sectors such as healthcare and finance. Nonetheless, programming these applications within the FHE framework is complex and demands extensive cryptographic expertise to guarantee correctness, performance, and security. In this paper, we present ANT-ACE, a production-quality, open-source FHE compiler designed to automate neural network inference on encrypted data. ANT-ACE accepts ONNX models and generates C/C++ programs, leveraging its custom open-source FHE library. We explore the design challenges encountered in the development of ANT-ACE, which is engineered to support a variety of input formats and architectures across diverse FHE schemes through a novel Intermediate Representation (IR) that facilitates multiple levels of abstraction. Comprising 44,000 lines of C/C++ code, ANT-ACE efficiently translates ONNX models into C/C++ programs for encrypted inference on CPUs, specifically utilizing the RNS-CKKS scheme. Preliminary evaluations on a single CPU indicate that ANT-ACE achieves significant speed enhancements in ResNet models, surpassing expert manual implementations and fulfilling our design goals.