PUMA Functional Simulator

System requirements

Below you can find the system requirements and versions tested.

Requirement	Version
Python	3.7.3
PyTorch	1.5.1

Quick start

test/pytorch_sample_cifar100.py is a sample testing script for CIFAR-100 dataset.

Conv2d_mvm and Linear_mvm are custom layers defined in pytorch_mvm_class_vX.py for running the conv2d and linear layers with the functional simulator backend.

models/resnet18_mvm.py is a model specification of resnet18 that uses custom layers - conv2d_mvm and linear_mvm.

To run a pretrained model:

cd test
python pytorch_sample_cifar100.py -b <batch_size> --pretrained <my_trained_model> --gpus <gpu ids>

Supported configuration parameters

parameters	Meaning	default value
bit_slice	# of bit-slicing for weight (1, 2, 4, 8)	2
bit_stream	# of bit-stream for input (1, 2, 4, 8)	1
weight_bits	# of bits for fixed point of weight (16, 32)	16
weight_bit_frac	# of bits for fraction part of weight	16 -> 12 / 32 -> 24
input_bits	# of bits for fixed point of input (16, 32)	16
input_bit_frac	# of bits for fraction part of input	16 -> 12 / 32 -> 24
adc_bit	# of adc bits (1, 2, ... )	9
acm_bits	# of bit of output	16
acm_bit_frac	# of bits for fraction partof output	16 -> 12 / 32 -> 24

HalfTensor Support

Gives at least 25% speedup with minimal change in accuracy (~0.1%). To enable, uncomment the following:

• src/pytorch_mvm_class_v3.py : Lines with '#uncomment for FP16' under Conv2d_mvm and Linear_mvm functions to set default tensor to torch.half()
• src/config.py : Change the geniex model to .half() when using non_ideality=True

Running NN models

CIFAR-100:

python3 pytorch_sample_cifar100.py -i <True for nonideal, False for ideal> -b <batch-size> --pretrained models/resnet20fp_cifar10.pth.tar --evaluate

Required model files:

• resnet20.py
• resnet20_mvm.py
• __init__.py

Note: resnet20_mvm.py has specifications for frac_bits for weights and inputs. Please change it inside the file.

ImageNet

python3 pytorch_sample_imnet.py -i <True for nonideal, False for ideal> -b <batch-size> --pretrained models/resnet18_imnet_fp.pth.ar --evaluate

Required model files:

• resnet18_imnet.py
• resnet18_imnet_mvm.py
• __init__.py

Note: resnet18_imnet_mvm.py has specifications for frac_bits for weights and inputs. Please change it inside the file.

Authors

Aayush Ankit, Dong-Eun Kim, Indranil Chakraborty, Mustafa Ali, Shubham Negi