NMAX is a general purpose Neural Inferencing Engine that can run any type of NN from simple fully connected DNN to RNN to CNN and can run multiple NNs at a time. It has demonstrated excellent inference efficiency, delivering more throughput on tough models for less $, less watts.

nnMAX is programmed with TensorFlow Lite and ONNX. Numerics supported are INT8, INT16 and BFloat16 and can be mixed layer by layer to maximize prediction accuracy. INT8/16 activations are processed at full rate; BFloat16 at half rate. Hardware converts between INT and BFloat as needed layer by layer. 3×3 Convolutions of Stride 1 are accelerated by Winograd hardware: YOLOv3 is 1.7x faster, ResNet-50 is 1.4x faster. This is done at full precision. Weights are stored in non-Winograd form to keep memory bandwidth low. nnMAX is a tile architecture any throughput required can be delivered with the right amount of SRAM for your model.