DPA on quasi delay insensitive asynchronous circuits: formalization and improvement

The purpose of this paper is to formally specify a flow devoted to the design of Differential Power Analysis (DPA) resistant QDI asynchronous circuits. The paper first proposes a formal modeling of the electrical signature of QDI asynchronous circuits. The DPA is then applied to the formal model in order to identify the source of leakage of this type of circuits. Finally, a complete design flow is specified to minimize the information leakage. The relevancy and efficiency of the approach is demonstrated using the design of an AES crypto-processor.Comment: Submitted on behalf of EDAA (http://www.edaa.com/

Adaptive Latency Insensitive Protocols

Latency-insensitive design copes with excessive delays typical of global wires in current and future IC technologies. It achieves its goal via encapsulation of synchronous logic blocks in wrappers that communicate through a latency-insensitive protocol (LIP) and pipelined interconnects. Previously proposed solutions suffer from an excessive performance penalty in terms of throughput or from a lack of generality. This article presents an adaptive LIP that outperforms previous static implementations, as demonstrated by two relevant cases — a microprocessor and an MPEG encoder — whose components we made insensitive to the latencies of their interconnections through a newly developed wrapper. We also present an informal exposition of the theoretical basis of adaptive LIPs, as well as implementation detail

Effect of winding harmonics on the asynchronous torque of a single-phase line start permanant-magnet motor

This paper presents an analytical method for calculating the effect of winding harmonics on the asynchronous torque of a single-phase line-start permanent-magnet motor. The method is an extension of earlier work, which combines symmetrical-component analysis with dq-axis theory to model the various components of forward and backward rotating fields. The effect of individual winding harmonics is brought out both theoretically and experimentally, by comparing calculated and measured torque/speed characteristics for a series of six motors with different distributions of turns in both the main and auxiliary windings

Asynchronous spiking neurons, the natural key to exploit temporal sparsity

Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms