  This paper introduces NeuroBlend, a novel neural network architecture
featuring a unique building block known as the Blend module. This module
incorporates binary and fixed-point convolutions in its main and skip paths,
respectively. There is a judicious deployment of batch normalizations on both
main and skip paths inside the Blend module and in between consecutive Blend
modules. Additionally, we present a compiler and hardware architecture designed
to map NeuroBlend models onto FPGA devices, aiming to minimize inference
latency while maintaining high accuracy. Our NeuroBlend-20 (NeuroBlend-18)
model, derived from ResNet-20 (ResNet-18) trained on CIFAR-10 (CIFAR-100),
achieves 88.0\% (73.73\%) classification accuracy, outperforming
state-of-the-art binary neural networks by 0.8\% (1.33\%), with an inference
time of 0.38ms per image, 1.4x faster than previous FPGA implementation for
BNNs. Similarly, our BlendMixer model for CIFAR-10 attains 90.6\%
accuracy(1.59\% less than full precision MLPMixer), with a 3.5x reduction in
model size compared to full precision MLPMixer. Furthermore, leveraging DSP
blocks for 48-bit bitwise logic operations enables low-power FPGA
implementation, yielding a 2.5x reduction in power consumption.
