High-performance image classification models involve massive computation and an energy cost that are unaffordable for resource-limited platforms. As a solution, reservoir computing based on cellular automata has been proposed, but there is still room for improvement in terms of classification cost. This research builds on the previous work introducing enhancements at both the algorithmic and architectural level. Using a random forest classifier with binary features completely eliminates multiplication operations and 97% of addition operations. Also, memory usage can be decreased by pruning 82% of the least relevant augmented features. An architecture with an increased level of parallelism which processes images in a single pass reduces memory accesses, and reduces 60% of logic by optimizing FPGA mapping. These speed, power, and memory optimizations come at an accuracy tradeoff of a mere 0.6%
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