We propose GForce, tackling the latency issue from the root causes instead of approximating non-linear computations. With the SWALP training approach (ICML 2019), we propose stochastic rounding and truncation (SRT) layers, which fuse quantization with dequantization between non-linear and linear layers and free us from floating-point operations for efficiency. They also ensure high accuracy while working over the severely-finite cryptographic field. We further propose a suite of GPU-friendly secure online/offline protocols for common operations, including comparison and wrap-around handling, which benefit non-linear layers, including our SRT.
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