A Configurable BNN ASIC using a Network of Programmable Threshold Logic Standard Cells

TL;DR

This paper introduces TULIP, an energy-efficient ASIC architecture for binary neural networks that uses reconfigurable mixed-signal neurons, achieving three times the energy efficiency of conventional MAC-based designs.

Contribution

The paper presents a novel reconfigurable binary neuron implemented as a standard cell, along with algorithms for mapping BNNs onto a SIMD-based ASIC architecture, improving energy efficiency.

Findings

TULIP achieves 3x energy efficiency over conventional designs.

Reconfigurable binary neurons can implement all BNN operations.

No penalty in performance, area, or accuracy compared to traditional ASICs.

Abstract

This paper presents TULIP, a new architecture for a binary neural network (BNN) that uses an optimal schedule for executing the operations of an arbitrary BNN. It was constructed with the goal of maximizing energy efficiency per classification. At the top-level, TULIP consists of a collection of unique processing elements (TULIP-PEs) that are organized in a SIMD fashion. Each TULIP-PE consists of a small network of binary neurons, and a small amount of local memory per neuron. The unique aspect of the binary neuron is that it is implemented as a mixed-signal circuit that natively performs the inner-product and thresholding operation of an artificial binary neuron. Moreover, the binary neuron, which is implemented as a single CMOS standard cell, is reconfigurable, and with a change in a single parameter, can implement all standard operations involved in a BNN. We present novel algorithms for mapping arbitrary nodes of a BNN onto the TULIP-PEs. TULIP was implemented as an ASIC in TSMC 40nm-LP technology. To provide a fair comparison, a recently reported BNN that employs a conventional MAC-based arithmetic processor was also implemented in the same technology. The results show that TULIP is consistently 3X more energy-efficient than the conventional design, without any penalty in performance, area, or accuracy.