Ristretto: Changes in V2

Commit fc109ba: On June 26, we updated our code on Github. This new version introduces some significant changes which might affect older versions of Ristretto:

• Ristretto network description files:
  •  precision field: the quantization modes are DYNAMIC_FIXED_POINT, MINIFLOAT and INTEGER_POWER_OF_2_WEIGHTS.
• Ristretto layers:
  • New DeconvolutionRistrettoLayer for applications like semantic segmentation.
  • Quantize layer inputs and layer outputs (previously: only quantize layer outputs).
  • Remove DataRistrettoLayer since images can be quantized in first convolutional layer.
• Layers without multiplications:
  • For precision = INTEGER_POWER_OF_2_WEIGHTS, the layer activations are now in dynamic fixed point.
• Quantization of layer activations
  • In older Ristretto versions, only the layer outputs were quantized. With this new version, layer inputs and layer outputs are in reduced word width format.
  • For dynamic fixed point approximation, we introduce 2 new protobuffer fields:
    • bw_layer_in for the word width of layer inputs
    • fl_layer_in for the fractional length of layer inputs
• Ristretto Demo on SqueezeNet:
  • We renamed the models/SqueezeNet/demo folder (new: models/SqueezeNet/RistrettoDemo)
  • We renamed the demo shell scripts in examples/ristretto/
• Ristretto Documentation:
  • We removed the docs/ristretto/ folder. All documentation is available on this web page.
• Refactoring in code base:
  • We improved the naming of class functions and variables for clarification:
  • “fixed point” -> “dynamic fixed point”
  • “mini floating point” -> “minifloat”
  • “power-2-weights” -> “integer power of 2 weights”
• Updated documentation
• Merge with upstream: This Ristretto version is merged with official Caffe commit f28f5ae.