We use standard hardware, with some special attention paid to making sure we maximally leverage AVX2/AVX512. The computation is naively parallel and very simple, so better hardware doesn't seem terribly likely to decrease costs. If anything would work, our bet is on GPU's, which seem to offer better memory bandwidth and are widely commercially available.
Comments
We use standard hardware, with some special attention paid to making sure we maximally leverage AVX2/AVX512. The computation is naively parallel and very simple, so better hardware doesn't seem terribly likely to decrease costs. If anything would work, our bet is on GPU's, which seem to offer better memory bandwidth and are widely commercially available.
FHE for ML is indeed not super practical yet. There are some recent works that get quite close: CryptGPU (https://arxiv.org/pdf/2104.10949.pdf) and Piranha (https://www.usenix.org/system/files/sec22-watson.pdf) both leverage GPU's for private ML (and even some MPC!).