We test the code using rustc 1.47.0. Use rustup override set 1.47.0 to specify the rust version for compilation. For the newer version implementation of ZEN in zen-arkworks, please use rustup override set 1.51.0 for cargo build.

• Under directory ZEN/zk-ml-private-model-baseline/, run mkdir test-data and cargo run --bin gen_data to generate the mock inputs for baseline and microbenchmark purposes only. For optimization level 3, we load real quantization parameters generated from ZEN/numpyInferenceEngine/XXNet/. For example, cd to ZEN/numpyInferenceEngine/LeNet_CIFAR10 and run python3.8 LeNet_end_to_end_quant.py --model LeNet_Small. The generated quantized parameters are located at ZEN/numpyInferenceEngine/LeNet_CIFAR10/LeNet_CIFAR_pretrained/. For easily reproducing the results, we have saved a copy of parameters for all combinations of model and dataset in director ZEN/zk-ml-private-model/pretrained_model/

REMINDER: before benchmarking, please use the corresponding num_window parameter. For different input image size, the commitment setting is different. Refer to pedersen_commit.rs and please ensure you use the correct num_window parameter for corresponding dataset.

• Pedersen input size = window_size * num_window / 8
• pub const PERDERSON_WINDOW_SIZE: usize = 25; // this is for 28X28X1 MNIST input
• pub const PERDERSON_WINDOW_SIZE: usize = 100; // this is for 32X32X3 CIFAR u8 input
• pub const PERDERSON_WINDOW_SIZE: usize = 100; // this is for 46X56X1 FACE u8 input

• cargo run --bin microbench_conv_layered_optimization_by_kernel_size --release 2>/dev/null
• cargo run --bin microbench_fc_layered_optimization --release 2>/dev/null

• cargo run --bin microbench_SIMD_by_batch_size --release 2>/dev/null

• cargo run --bin microbench_lenet_small_cifar_naive --release 2>/dev/null
• cargo run --bin microbench_lenet_small_cifar_op1 --release 2>/dev/null
• cargo run --bin microbench_lenet_small_cifar_op2 --release 2>/dev/null
• cargo run --bin microbench_lenet_small_cifar_op3 --release 2>/dev/null

• cargo run --bin shallownet_naive_mnist --release 2>/dev/null
• cargo run --bin lenet_small_naive_pedersen_cifar --release 2>/dev/null
• cargo run --bin lenet_medium_naive_pedersen_cifar --release 2>/dev/null
• cargo run --bin lenet_small_naive_pedersen_face --release 2>/dev/null
• cargo run --bin lenet_medium_naive_pedersen_face --release 2>/dev/null
• cargo run --bin lenet_large_naive_pedersen_face --release 2>/dev/null

• cargo run --bin shallownet_optimized_pedersen_mnist --release 2>/dev/null
• cargo run --bin lenet_small_optimized_pedersen_cifar --release 2>/dev/null
• cargo run --bin lenet_medium_optimized_pedersen_cifar --release 2>/dev/null
• cargo run --bin lenet_small_optimized_pedersen_face --release 2>/dev/null
• cargo run --bin lenet_medium_optimized_pedersen_face --release 2>/dev/null
• cargo run --bin lenet_large_optimized_pedersen_face --release 2>/dev/null

• cargo run --bin shallownet_accuracy --release 2>/dev/null
• cargo run --bin lenet_small_cifar_accuracy --release 2>/dev/null
• cargo run --bin lenet_medium_cifar_accuracy --release 2>/dev/null
• cargo run --bin lenet_small_face_accuracy --release 2>/dev/null
• cargo run --bin lenet_medium_face_accuracy --release 2>/dev/null
• cargo run --bin lenet_large_face_accuracy --release 2>/dev/null