MIL
RU/EN
  • COMPRESSA:
    DL Models Compression Platform

    Production-ready solution for infrastructure optimization in several weeks, instead of several months

    Great variability of supported model architectures
  • ML Infrastructure cost reduction by decreasing the computational complexity of DL models
  • On-device ML Infrastructure transfer by compression the DL models into device limitations
BUSINESS CASES
1
RAM, Energy, CPU/GPU lower consumption while models inference
2
Transfer high-quality and complex models on device
3
Models inference on low-bit CPU
4
Speeding up calculations
Learn More about Platform 
SOLVING METHODS
  • Post-training and Low-Bit Quantisation (Adaround, GDRQ, LSQ, own modification of LSQ, APoT, Symmetric, Asymmetric, etc)
  • Pruning and Knowledge Distillation (HRank, CUP, Cluster-based, Magnitude)
  • Device Placement
  • DL Optimization Solvers
EXPERIENCE & EXAMPLES
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