  We propose AIDA, an inference engine for accelerating fully-connected (FC)
layers of Deep Neural Network (DNN). AIDA is an associative in-memory
processor, where the bulk of data never leaves the confines of the memory
arrays, and processing is performed in-situ. AIDA area and energy efficiency
strongly benefit from sparsity and lower arithmetic precision. We show that
AIDA outperforms the state of art inference accelerator, EIE, by 14.5x (peak
performance) and 2.5x (throughput).
