  The scaling hypothesis motivates the expansion of models past trillions of
parameters as a path towards better performance. Recent significant
developments, such as GPT-3, have been driven by this conjecture. However, as
models scale-up, training them efficiently with backpropagation becomes
difficult. Because model, pipeline, and data parallelism distribute parameters
and gradients over compute nodes, communication is challenging to orchestrate:
this is a bottleneck to further scaling. In this work, we argue that
alternative training methods can mitigate these issues, and can inform the
design of extreme-scale training hardware. Indeed, using a synaptically
asymmetric method with a parallelizable backward pass, such as Direct Feedback
Alignement, communication needs are drastically reduced. We present a photonic
accelerator for Direct Feedback Alignment, able to compute random projections
with trillions of parameters. We demonstrate our system on benchmark tasks,
using both fully-connected and graph convolutional networks. Our hardware is
the first architecture-agnostic photonic co-processor for training neural
networks. This is a significant step towards building scalable hardware, able
to go beyond backpropagation, and opening new avenues for deep learning.
