DeepSparse v0.6.0

New Features:

• DeepSparse Engine optimized for Sparse FP32 BERT.
  • Optimized BERT model collection now in the SparseZoo.
  • Performance improvement example includes 5x increased throughput on PruneBERT (281 seq/sec) compared to dense BERT (53 seq/sec) at batch size 32 and sequence length 128 (AWS c5.12xlarge).
• Optimized Tanh operator support provided.
• Hugging Face transformers pipeline APIs added for NLP models.
• Hugging Face transformers examples added for benchmarking, deploying, and sample application.
• Ultralytics YOLOv5 example support added.

Changes:

• Performance improvements made for:
  - all networks when running on multi-socket machines, especially those with large outputs.
  - batched Softmax and Reduce operators with many threads available.
  - Reshape operators when multiple dimensions are combined into one or one dimension is split into multiple.
  - stacked matrix multiplications by supporting more input layouts.
• YOLOv3 example integration was generalized to ultralytics-yolo in support of both V3 and V5.

Resolved Issues:

• Engine now runs on architectures with more than one NUMA node per socket.

Known Issues:

• None