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Sparse Processing Unit (SPU)

The inventor of the dual sparsity algorithm, Moffett AI has the world's leading sparse computing techniques with more than 30 patents worldwide. The company creates a new generation of AI computing platform with hardware and software co-design to achieve order-of-magnitude acceleration of computing performance, reducing latency and low TCO.

The result in Byte MLPerf has demonstrated the potential of sparse computing in inference performance and energy efficiency, which leads to a lower total cost of ownership (TCO).

For Byte MLPerf, Moffett has submitted performance results of the following models.

Model Precision Sparsity* QPS Dataset Metric name Metric value
resnet50-torch-fp32 INT8 16x 52423 Open Imagenet Top-1 76.61%
bert-torch-fp32 INT8/BF16 16x 7738 Open Squad 1.1 F1 Score 86.09
albert-torch-fp32 INT8/BF16 16x 10824 Open Squad 1.1 F1 Score 87.66
roberta-torch-fp32 INT8/BF16 16x 8107 Open Squad 1.1 F1 Score 86.63
conformer-encoder-onnx-fp32 INT8/BF16 8x 8211 Fake Dataset Mean Diff 1.50

* The sparsity is determined by the ratio of time spent on Matmul operations compared to the overall time of model inference.

Besides the performance results, energy efficiency is another significant highlight of Moffett's devices. For example, the peak power consumption of S30 is merely 250W.

The Antoum architecture through hardware and software co-design and Moffett's original sparsity algorithm are the reasons to achieve great performance with high energy efficiency.

The accelerators for AI inference applications in data centers are equipped with Moffett's 1st generation Antoum processor - the first commercial AI processor with 32x sparsity in the world.

Besides the sparse processing units (SPU) for native sparse convolution and matrix computing in Antoum, the processor also integrates a Vector Processing Unit (VPU), which enables flexible programmability to keep up with the fast evolution of AI models.

Also, the on-chip Video Codec, which supports 192-way 1080p video decoding at 30 FPS, and the JPEG decoder, which supports 1080p image decoding up to 6960 FPS, provide an end-to-end capability for video and image inference workloads.

Moffett provides three SKUs of sparse computing devices, namely S4, S10, and S30. Based on the computing power of S4, S10 and S30 are designed to be 2 times and 3 times the computing power of S4 respectively.

How to run

1. Environmental preparation

Download offline image

wget moffett-oss-bucket01.oss-cn-shenzhen.aliyuncs.com/byte-perf/byte-perf-2.3.2-20230721.tar

Load offline image

docker load -i byte-perf-2.3.2-20230721.tar

Decompress the model data package

wget moffett-oss-bucket01.oss-cn-shenzhen.aliyuncs.com/byte-perf/byte-perf-data.tar.gz
tar -zxvf byte-perf-data.tar.gz

2. Create docker container

notes: --shm-size="300g" is recommended to be 95% of the total memory of the host.

cd byte-perf-data
sudo docker run -itd \
    --privileged \
    --cap-add=ALL \
    --net=host \
    -v /dev:/dev \
    -v /usr/src:/usr/src \
    -v /lib/modules:/lib/modules \
    -v $PWD/package:/home/moffett/workspace/package \
    -e ROOT_PASS=moffett \
    --shm-size="300g" \
    --name byte-perf-2023 \
    byte-perf:2.3.2-20230721

3. Environment initialization

Environment initialization please operate in the container.

docker exec -it byte-perf-2023 /bin/bash

Install drivers and load firmware

cd /usr/local/sola/driver/bin/
sudo ./setup.sh

Device basic information verification

mf-smi is a command line utility that can view various information of S30, such as card number, usage, temperature, power consumption, etc. After the driver is successfully installed, execute mf-smi to view the basic information of the device.

mf-smi

4. Run byte-mlperf task in container

cd /home/moffett/workspace/package/bytemlperf

# config spu-backend env 
export PYTHONPATH=$PYTHONPATH:/home/moffett/workspace/spu-backend-release/ubuntu18.04-gcc7.5.0-x86_64/lib/

# conformer
python3 launch.py --task conformer-encoder-onnx-fp32 --hardware_type SPU

# albert
python3 launch.py --task albert-torch-fp32 --hardware_type SPU

# bert
python3 launch.py --task bert-torch-fp32 --hardware_type SPU

# roberta
python3 launch.py --task roberta-torch-fp32 --hardware_type SPU

# resnet50
python3 launch.py --task resnet50-torch-fp32 --hardware_type SPU

Contact us

If you are interested in further information about the products, please contact the email: [email protected]