Compass: A scalable simulator for an architecture for cognitive computing

Abstract

Abstract-Inspired by the function, power, and volume of the organic brain, we are developing TrueNorth, a novel modular, non-von Neumann, ultra-low power, compact architecture. TrueNorth consists of a scalable network of neurosynaptic cores, with each core containing neurons, dendrites, synapses, and axons. To set sail for TrueNorth, we developed Compass, a multi-threaded, massively parallel functional simulator and a parallel compiler that maps a network of long-distance pathways in the macaque monkey brain to TrueNorth. We demonstrate near-perfect weak scaling on a 16 rack IBM® Blue Gene®/Q (262144 CPUs, 256 TB memory), achieving an unprecedented scale of 256 million neurosynaptic cores containing 65 billion neurons and 16 trillion synapses running only 388× slower than real time with an average spiking rate of 8.1 Hz. By using emerging PGAS communication primitives, we also demonstrate 2× better real-time performance over MPI primitives on a 4 rack Blue Gene/P (16384 CPUs, 16 TB memory).I. INTRODUCTION The brain and modern computers have radically different architectures [1] suited for complementary applications. Modern computing posits a stored program model, traditionally implemented in digital, synchronous, serial, centralized, fast, hardwired, general-purpose, brittle circuits, with explicit memory addressing imposing a dichotomy between computation and data. In stark contrast, the brain uses replicated computational units of neurons and synapses implemented in mixedmode analog-digital, asynchronous, parallel, distributed, slow, reconfigurable, specialized, and fault-tolerant biological substrates, with implicit memory addressing blurring the boundary between computation and data [2]. It is therefore no surprise that one cannot emulate the function, power, volume, and realtime performance of the brain within the modern computer architecture. This task requires a radically novel architecture.Today, one must still build novel architectures in CMOS technology, which has evolved over the past half-century to serve modern computers and which is not optimized for delivering brain-like functionality in a compact, ultra-lowpower package. For example, biophysical richness of neurons and 3D physical wiring are out of the question at the very outset. We need to shift attention from neuroscientific richness that is sufficient to mathematical primitives that are necessary. A question of profound relevance to science, technology, business, government, and society is how closely can one approximate the function, power, volume, and real-time performance of the brain within the limits of modern technology.

Development and Practical Application of Neural Information Technologies, Patterns

“…TrueNorth is a brain-inspired chip architecture built from an interconnected network of lightweight neurosynaptic cores (Figure 8) [5,6]. TrueNorth implements "gray matter" short-range connections with an intra-core crossbar memory and "white matter" long-range connections through an inter-core spike-based message-passing network.…”

Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks

“…7 (Merolla, Arthur, Alvarez-Icaza et al, 2014). Differently from the other architectures discussed so far, the focus is not on simulating the brain for research purposes but on creating an efficient tool for concrete applications (Preissl et al, 2012). Nevertheless, the chip operates in biological real-time.…”

“…Completed corelets are stored in a Corelet Library for later reuse. Finally, the application development is supported by a Corelet Laboratory, which also enables the execution of TrueNorth programs on the Compass simulator (Preissl et al, 2012).…”

Computational modeling of psychiatric illnesses via well-defined neurophysiological and neurocognitive biomarkers

“…Second, the computing capacity to construct simulations of this level of detail has become much more readily available (and affordable). As a result, large, multi-million neuron simulations-which are most efficiently implemented on parallel computing platforms, such as computer clusters or supercomputers-have been created; examples include the Blue Brain project (Markram 2006), the Spaun model of Eliasmith et al (2012), and the DARPA funded Synapse project (Preissl et al 2012). Typically configured hardware systems for this kind of work have decreased in price by a factor of ten or more in the past decade, and are now well within reach of many laboratories.…”