Presentation
FHE-CGRA: Enable Efficient Acceleration of Fully Homomorphic Encryption on CGRAs
DescriptionFully Homomorphic Encryption (FHE) is a privacy-preserving technique that allows computation directly on encrypted data. In this work, we investigate execution the of FHE machine learning (ML) applications. We show that the runtime hardware reconfigurability of the underlying execution units of homomorphic operations is highly desirable for efficient hardware resource utilization. Based on the observation, we propose FHE-CGRA, a coarse-grained reconfigurable architecture (CGRA) acceleration framework for end-to-end homomorphic applications. The experiment shows that FHE-CGRA achieves up to 8.15x speedup against a conventional CGRA for accelerating FHE-encrypted convolution neural network (FHE-CNN) models, and 16.48x power efficiency w.r.t. the state-of-the-art FPGA.
Event Type
Research Manuscript
TimeTuesday, June 254:30pm - 4:45pm PDT
Location3012, 3rd Floor
Design
SoC, Heterogeneous, and Reconfigurable Architectures