Heterogeneous Acceleration of Neural-Mass Models towards Digital Brain Twins

Abstract

The human brain is arguably the most complex system we know of. For centuries, understanding the brain and the way it works has been of great concern to scientists. In 1952, the first mathematical model of a neuron was developed by Hodgkin and Huxley. This work pioneered the field of computational neuroscience, which studies the organization of the brain and the way it processes information. Modeling and simulation of neurons and small brain regions at the cellular level offer great insights into the details of brain mechanics, however, they are far away from clinical applications.

The Virtual Brain (TVB) is a simulation framework that takes a different approach to brain modeling. TVB reduces complexity at the micro level to achieve the macro level of brain modeling and simulation. This approach in brain modeling is referred to as Large-scale Brain Network or Neural-Mass Modeling. Additionally, TVB incorporates individual brain-imaging data with the models to achieve personalized, patient-specific digital brain twins. The digital brain twins can be used in clinical settings such as neurosurgeries and real-time brain-interface systems. However, building and utilizing such large-scale brain models requires high performance and low latency in terms of computation.

Today, heterogeneous architectures, due to their enhanced performance, energy efficiency, and better flexibility are being utilized increasingly in computing systems ranging from low-power edge devices to high-performance cloud infrastructures. Versal Adaptive Compute Acceleration Platform (ACAP) is a high-performance, heterogeneous computing platform developed by Xilinx/AMD. Versal ACAP offers an array of vector processors, specialized in artificial intelligence (AI) and signal processing workloads, next to the traditional programmable logic with extensive memory resources. The large-scale neural-mass simulation problem at hand has heterogeneous computational needs, which makes it interesting to see how it maps to the Versal ACAP. In this work, we explore the benefits and challenges of using heterogeneous systems (specifically, the Versal ACAP) in the high-performance workload of large-scale neural-mass simulation.

We designed and implemented a dataflow-style system for large-scale brain network simulation on the Versal ACAP. Two different versions of this system, namely AIE-Only and Heterogeneous, were developed in order to explore the capabilities of the Versal ACAP when accelerating our application. Compared to the current GPU version of TVB, the Heterogeneous implementation performed on average around 4× slower in terms of throughput. However, the Versal ACAP Heterogeneous implementation delivered around 13× lower latency, two orders of magnitude better energy efficiency, and around 2× better power efficiency compared to the GPU version of TVB. Although the Heterogeneous implementation falls short in terms of throughput compared to the GPU version, its lower latency and energy efficiency make it suitable for real-time applications that might use large-scale neural-mass modeling such as virtual surgery or brain interface devices.