[osaariki]

I've done work on low-latency FHE neural network inferencing [1] and we estimated the FLOPS to be approximately 4 times that of an Intel 8087 floating point coprocessor [2]. This was for a LeNet-5 network on the MNIST dataset with multicore evaluation on a workstation class machine.

My view is that this is already fast enough to support use cases that really need the unique capabilities of FHE. Since this work we've been focused on making FHE more usable with compilers and tooling [3]. Currently most FHE is being programmed like that Intel 8087 was: with the equivalent of assembly by directly calling functions in FHE libraries to perform arithmetic and crypto operations. Imagine having to do register allocation by hand for all of your code. The EVA compiler [4] is meant to be like a "C compiler for FHE", hiding low-level crypto concerns and providing common optimizations.

[1] "CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs": https://arxiv.org/pdf/1810.00845.pdf

[2] https://en.wikipedia.org/wiki/Intel_8087

[3] "EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation": https://arxiv.org/pdf/1912.11951.pdf

[4] https://github.com/microsoft/EVA