  In this paper, we present a deep reinforcement learning platform named FIXAR
which employs fixed-point data types and arithmetic units for the first time
using a SW/HW co-design approach. Starting from 32-bit fixed-point data,
Quantization-Aware Training (QAT) reduces its data precision based on the range
of activations and performs retraining to minimize the reward degradation.
FIXAR proposes the adaptive array processing core composed of configurable
processing elements to support both intra-layer parallelism and intra-batch
parallelism for high-throughput inference and training. Finally, FIXAR was
implemented on Xilinx U50 and achieves 25293.3 inferences per second (IPS)
training throughput and 2638.0 IPS/W accelerator efficiency, which is 2.7 times
faster and 15.4 times more energy efficient than those of the CPU-GPU platform
without any accuracy degradation.
