  In the recent years, heterogeneous machine learning accelerators have become
of significant interest in science, engineering and industry. The major
processing speed bottlenecks in these platforms come from (a) an electronic
data interconnect; (b) an electro-optical interface update rate. In this light,
information compression implemented in native to incoming data optical domain
could mitigate both problems mentioned above by reducing the demand on data
throughput at the camera side and beyond. In this paper we present an optical
hashing and compression scheme that is based on SWIFFT - a post-quantum hashing
family of algorithms. High degree optical hardware-to-algorithm homomorphism
allows to optimally harvest well-understood potential of free-space processing:
innate parallelism, low latency tensor by-element multiplication and Fourier
transform. The algorithm can provide several orders of magnitude increase in
processing speed by replacing slow high-resolution CMOS cameras with ultra fast
and signal-triggered CMOS detector arrays. Additionally, the information
acquired in this way will require much lower transmission throughput, less
\emph{in silico} processing power, storage, and will be pre-hashed facilitating
cheap optical information security. This technology has a potential to allow
heterogeneous convolutional 4f classifiers get closer in performance to their
fully electronic counterparts.
