Bernard Brezzo

TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.

TL;DR: This work developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture, and successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition.

TL;DR: A new architecture is proposed to overcome scalable learning algorithms for networks of spiking neurons in silicon by combining innovations in computation, memory, and communication to leverage robust digital neuron circuits and novel transposable SRAM arrays.

TL;DR: This paper presents a Field Programmable Gate Array (FPGA) based acceleration engine for database operations in analytics queries that provides a mechanism for a DBMS to seamlessly harness the FPGA compute power without requiring any changes in the application or the existing data layout.

TL;DR: 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.