Scott Lekuch

Real-time scalable cortical computing at 46 giga-synaptic OPS/watt with ~100× speedup in time-to-solution and ~100,000× reduction in energy-to-solution

Drawing on neuroscience, we have developed a parallel, event-driven kernel for neurosynaptic computation, that is efficient with respect to computation, memory, and communication. Building on the previously demonstrated highly optimized software expression of the kernel, here, we demonstrate True North, a co-designed silicon expression of the kernel. True North achieves five orders of magnitude reduction in energy to-solution and two orders of magnitude speedup in time-to solution, when running computer vision applications and complex recurrent neural network simulations. Breaking path with the von Neumann architecture, True North is a 4,096 core, 1 million neuron, and 256 million synapse brain-inspired neurosynaptic processor, that consumes 65mW of power running at real-time and delivers performance of 46 Giga-Synaptic OPS/Watt. We demonstrate seamless tiling of True North chips into arrays, forming a foundation for cortex-like scalability. True Norths unprecedented time-to-solution, energy-to-solution, size, scalability, and performance combined with the underlying flexibility of the kernel enable a broad range of cognitive applications.

Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications

This paper describes the hardware and software ecosystem encompassing the brain-inspired TrueNorth processor – a 70mW reconfigurable silicon chip with 1 million neurons, 256 million synapses, and 4096 parallel and distributed neural cores. For systems, we present a scale-out system loosely coupling 16 single-chip boards and a scale-up system tightly integrating 16 chips in a 4 × 4 configuration by exploiting TrueNorths native tiling. For software, we present an end-to-end ecosystem consisting of a simulator, a programming language, an integrated programming environment, a library of algorithms and applications, firmware, tools for deep learning, a teaching curriculum, and cloud enablement. For the scale-up systems we summarize our approach to physical placement of neural network, to reduce intra- and inter-chip network traffic. The ecosystem is in use at over 30 universities and government/corporate labs. Our platform is a substrate for a spectrum of applications from mobile and embedded computing to cloud and supercomputers.

A study in rapid prototyping: Leveraging software and hardware simulation tools in the bringup of system-on-a-chip based platforms

Traditional use of software and hardware simulators and emulators has been in efforts for chip level analysis and verification. However, prototyping and bringup requirements often demands system or platform level integration and analysis requiring new uses of these traditional pre-silicon methods along with novel interpretations of existing hardware to prototype some functions matching behaviors of future systems. In order to demonstrate the versatility and breadth of the pre-silicon environments in our systems lab, ranging from functional instruction set software simulators to Field Programmable Gate Array (FPGA) chip logic implementations to integrated systems of existing hardware built to mimic key functional aspects of the future platforms, we present our experiences with platform level verification, analysis and early software development/enablement for an I/O attached network appliance system. More specifically, we show how simulation tools along with these early prototype systems were used to do chip level verification, early software development and even system level software testing for a System on a Chip processor attached as an I/O accelerator via Peripheral Component Interconnect Express (PCI Express) to a host system. Our experiences demonstrate that leveraging the full range of pre-silicon environment capabilities results in full system level integrated software test for a I/O attached platform prior to the availability of fully functional ASICs.