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In recent years, heterogeneous machine learning accelerators have become of significant interest to science, engineering,
and industry. At the same time, demand for data security has increased significantly, especially in the looming post-
quantum encryption era. From a hardware processing point of view, both are challenged by electronic capacitive
interconnect delay and energy, and, in the case of heterogeneous systems such as electronic-photonic accelerators,
by parasitic domain crossings. With analog optical AI accelerators having demonstrated high throughout potential
(TOPS to even POPS) and high operation efficiency (TOPS/W), they have not demonstrated the ability to perform AI
classification task on encrypted data.
Here, 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 in silico processing power,
storage, and will be pre-hashed facilitating cheap optical information security. This technology has the potential to
allow heterogeneous convolutional 4f classifiers to get closer in performance to their fully electronic counterparts
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