  Due to the boom in technical compute in the last few years, the world has
seen massive advances in artificially intelligent systems solving diverse
real-world problems. But a major roadblock in the ubiquitous acceptance of
these models is their enormous computational complexity and memory footprint.
Hence efficient architectures and training techniques are required for
deployment on extremely low resource inference endpoints. This paper proposes
an architecture for detection of alphabets in American Sign Language on an ARM
Cortex-M7 microcontroller having just 496 KB of framebuffer RAM. Leveraging
parameter quantization is a common technique that might cause varying drops in
test accuracy. This paper proposes using interpolation as augmentation amongst
other techniques as an efficient method of reducing this drop, which also helps
the model generalize well to previously unseen noisy data. The proposed model
is about 185 KB post-quantization and inference speed is 20 frames per second.
