Reliability-Aware Design for Nanometer-Scale Devices, January 2008

Continuous transistor scaling due to improvements in CMOS devices and manufacturing technologies is increasing processor power densities and temperatures; thus, creating challenges to maintain manufacturing yield rates and reliable devices in their expected lifetimes for latest nanometer-scale dimensions. In fact, new system and processor microarchitectures require new reliability-aware design methods and exploration tools that can face these challenges without significantly increasing manufacturing cost, reducing system performance or imposing large area overheads due to redundancy. In this paper we overview the latest approaches in reliability modeling and variability-tolerant design for latest technology nodes, and advocate the need of reliability-aware design for forthcoming consumer electronics. Moreover, we illustrate with a case study of an embedded processor that effec- tive reliability-aware design can be achieved in nanometer-scale devices through integral design approaches that covers modeling and exploration of reliability effects, and hardware-software architectural techniques to provide reliability-enhanced solutions at both microarchitectural- and system-level

Accelerating Neuromorphic Vision Algorithms for Recognition

ABSTRACT Video analytics introduce new levels of intelligence to automated scene understanding. Neuromorphic algorithms, such as HMAX, are proposed as robust and accurate algorithms that mimic the processing in the visual cortex of the brain. HMAX, for instance, is a versatile algorithm that can be repurposed to target several visual recognition applications. This paper presents the design and evaluation of hardware accelerators for extracting visual features for universal recognition. The recognition applications include object recognition, face identification, facial expression recognition, and action recognition. These accelerators were validated on a multi-FPGA platform and significant performance enhancement and power efficiencies were demonstrated when compared to CMP and GPU platforms. Results demonstrate as much as 7.6X speedup and 12.8X more power-efficient performance when compared to those platforms

A Low Power, Fully Event-Based Gesture Recognition System

We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS). The biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a fixed frame rate. This sparse, asynchronous data representation lets event-based cameras operate at much lower power than frame-based cameras. However, much of the energy efficiency is lost if, as in previous work, the event stream is interpreted by conventional synchronous processors. Here, for the first time, we process a live DVS event stream using TrueNorth, a natively event-based processor with 1 million spiking neurons. Configured here as a convolutional neural network (CNN), the TrueNorth chip identifies the onset of a gesture with a latency of 105 ms while consuming less than 200 mW. The CNN achieves 96.5% out-of-sample accuracy on a newly collected DVS dataset (DvsGesture) comprising 11 hand gesture categories from 29 subjects under 3 illumination conditions