Block Floating Point Support

[Andrew Stanford-Jason]

Hello all, I am interested in the possibility of supporting block floating point(BFP) representation of data/dynamic tensors. To be clear BFP is a quantisation representation where a tensor has a single floating point exponent for all elements. This is in contrast to floating point tensors where each element has an exponent and fixed point where all elements have a static exponent(typically common to all elements). 

My interests lie in supporting more traditional DSP through tflm. I would like to express, for example, an audio frontend, which can have enormous dynamic range, in TensorFlow. Using BFP allows much more efficient computation. Acoustic echo cancellation is a good example of where the signal of interest can be 60dB beneath the input signal(~10bits down).

I have plenty of experience implementing BFP DSP functions. From a high level BFP implementations of kernels are fixed point kernels with redundant sign bits and exponent management, i.e. they are implementable with integer arithmetic. 

So my questions are:

• is this possible?
• is anyone else interested in this?
• is this a roadmap feature?
• if not then what's the process? RFC?

Thank you

asj

[Advait Jain]

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