TaiBai: A fully programmable brain-inspired processor with topology-aware efficiency

TL;DR

TaiBai is a programmable brain-inspired processor that supports flexible network topologies and achieves over 200 times higher energy efficiency than GPUs for various tasks, enabling scalable and efficient brain simulation.

Contribution

It introduces a flexible topology encoding scheme, a multi-granularity instruction set, and a co-designed compiler for a brain-inspired processor, addressing rigidity and programmability limitations.

Findings

Over 200x energy efficiency compared to NVIDIA RTX 3090.

Supports arbitrary network architectures with reduced storage overhead.

Effective across tasks like speech recognition and brain-computer interfaces.

Abstract

Brain-inspired computing has emerged as a promising paradigm to overcome the energy-efficiency limitations of conventional intelligent systems by emulating the brain's partitioned architecture and event-driven sparse computation. However, existing brain-inspired chips often suffer from rigid network topology constraints and limited neuronal programmability, hindering their adaptability. To address these challenges, we present TaiBai, an event-driven, programmable many-core brain-inspired processor that leverages temporal and spatial spike sparsity to minimize bandwidth and computational overhead. TaiBai chip contains three key features: First, a brain-inspired hierarchical topology encoding scheme is designed to flexibly support arbitrary network architectures while slashing storage overhead for large-scale networks; Second, a multi-granularity instruction set enables programmability of brain-like spiking neuron or synapses with various dynamics and on-chip learning rules; Third, a co-designed compiler stack optimizes task mapping and resource allocation. After evaluating across various tasks, such as speech recognition, ECG classification, and cross-day brain-computer interface decoding, we found spiking neural networks embedded on the TaiBai chip could achieve more than 200 times higher energy efficiency than a standard NVIDIA RTX 3090 GPU at a comparable accuracy. These results demonstrated its high potentiation as a scalable, programmable, and ultra-efficient solution for both multi-scale brain simulation and brain-inspired computation.