AI algorithms demand an unprecedented level of compute. However, it is not the compute operations, but rather the underlying data motion operations that dominate energy costs. In this talk, we discuss how Femtosense employs sparsity and locality to mitigate energy costs. Sparsity is difficult to exploit on existing CPU and GPU-like architectures due to its irregular data access patterns. It is easy to lose any potential gains to the overhead necessary on existing architectures for sparse processing. Femtosense corrects this algorithm-architecture mismatch in its Sparse Processing Unit to enable large scale AI applications where previously considered impossible (e.g., the battery-powered edge).