  The recent surge of interest in Deep Neural Networks (DNNs) has led to
increasingly complex networks that tax computational and memory resources. Many
DNNs presently use 16-bit or 32-bit floating point operations. Significant
performance and power gains can be obtained when DNN accelerators support
low-precision numerical formats. Despite considerable research, there is still
a knowledge gap on how low-precision operations can be realized for both DNN
training and inference. In this work, we propose a DNN architecture, Deep
Positron, with posit numerical format operating successfully at $\leq$8 bits
for inference. We propose a precision-adaptable FPGA soft core for exact
multiply-and-accumulate for uniform comparison across three numerical formats,
fixed, floating-point and posit. Preliminary results demonstrate that 8-bit
posit has better accuracy than 8-bit fixed or floating-point for three
different low-dimensional datasets. Moreover, the accuracy is comparable to
32-bit floating-point on a Xilinx Virtex-7 FPGA device. The trade-offs between
DNN performance and hardware resources, i.e. latency, power, and resource
utilization, show that posit outperforms in accuracy and latency at 8-bit and
below.
