This paper presents TULIP, a new architecture for a variable precision Quantized Neural Network (QNN) inference. It is designed with the goal of maximizing energy efficiency per classification. TULIP is constructed by arranging a collection of unique processing elements (TULIP-PEs) in a single instruction multiple data (SIMD) fashion. Each TULIP-PE contains binary neurons that are interconnected using multiplexers. Each neuron also has a small dedicated local register connected to it. The binary neurons are implemented as standard cells and used for implementing threshold functions, i.e., an inner-product and thresholding operation on its binary inputs. The neurons can be reconfigured with a single change in the control signals to implement all the standard operations used in a QNN.

This paper presents novel algorithms for implementing the operations of a QNN on the TULIP-PEs in the form of a schedule of threshold functions. TULIP was implemented as an ASIC in TSMC 40nm- LP technology. A QNN accelerator that employs a conventional MAC-based arithmetic processor was also implemented in the same technology to provide a fair comparison. The results show that TULIP is 30-50X more energy-efficient than an equivalent design, without any penalty in performance, area, or accuracy. Furthermore, TULIP achieves these improvements without using traditional techniques such as voltage scaling or approximate computing. Finally, the paper also demonstrates how the run- time trade-off between accuracy and energy efficiency is done on the TULIP architecture.