On the Distribution of Control in Asynchronous Processor Architectures

Institute for Computing Systems ArchitectureThe effective performance of computer systems is to a large measure determined by the synergy between the processor architecture, the instruction set and the compiler. In the past, the sequencing of information within processor architectures has normally been synchronous: controlled centrally by a clock. However, this global signal could possibly limit the future gains in performance that can potentially be achieved through improvements in implementation technology. This thesis investigates the effects of relaxing this strict synchrony by distributing control within processor architectures through the use of a novel asynchronous design model known as a micronet. The impact of asynchronous control on the performance of a RISC-style processor is explored at different levels. Firstly, improvements in the performance of individual instructions by exploiting actual run-time behaviours are demonstrated. Secondly, it is shown that micronets are able to exploit further (both spatial and temporal) instructionlevel parallelism (ILP) efficiently through the distribution of control to datapath resources. Finally, exposing fine-grain concurrency within a datapath can only be of benefit to a computer system if it can easily be exploited by the compiler. Although compilers for micronet-based asynchronous processors may be considered to be more complex than their synchronous counterparts, it is shown that the variable execution time of an instruction does not adversely affect the compiler's ability to schedule code efficiently. In conclusion, the modelling of a processor's datapath as a micronet permits the exploitation of both finegrain ILP and actual run-time delays, thus leading to the efficient utilisation of functional units and in turn resulting in an improvement in overall system performance

Are We There Yet? Product Quantization and its Hardware Acceleration

Conventional multiply-accumulate (MAC) operations have long dominated computation time for deep neural networks (DNNs). Recently, product quantization (PQ) has been successfully applied to these workloads, replacing MACs with memory lookups to pre-computed dot products. While this property makes PQ an attractive solution for model acceleration, little is understood about the associated trade-offs in terms of compute and memory footprint, and the impact on accuracy. Our empirical study investigates the impact of different PQ settings and training methods on layerwise reconstruction error and end-to-end model accuracy. When studying the efficiency of deploying PQ DNNs, we find that metrics such as FLOPs, number of parameters, and even CPU/GPU performance, can be misleading. To address this issue, and to more fairly assess PQ in terms of hardware efficiency, we design the first custom hardware accelerator to evaluate the speed and efficiency of running PQ models. We identify PQ configurations that are able to improve performance-per-area for ResNet20 by 40%-104%, even when compared to a highly optimized conventional DNN accelerator. Our hardware performance outperforms recent PQ solutions by 4x, with only a 0.6% accuracy degradation. This work demonstrates the practical and hardware-aware design of PQ models, paving the way for wider adoption of this emerging DNN approximation methodology

A Low-Power Two-Digit Multi-dimensional Logarithmic Number System Filterbank Architecture for a Digital Hearing Aid

This paper addresses the implementation of a filterbank for digital hearing aids using a multi-dimensional logarithmic number system (MDLNS). The MDLNS, which has similar properties to the classical logarithmic number system (LNS), provides more degrees of freedom than the LNS by virtue of having two, or more, orthogonal bases and the ability to use multiple MDLNS components or digits. The logarithmic properties of the MDLNS also allow for reduced complexity multiplication and large dynamic range, and a multiple-digit MDLNS provides a considerable reduction in hardware complexity compared to a conventional LNS approach. We discuss an improved design for a two-digit 2D MDLNS filterbank implementation which reduces power and area by over two times compared to the original design

A Network-based Asynchronous Architecture for Cryptographic Devices

Institute for Computing Systems ArchitectureThe traditional model of cryptography examines the security of the cipher as a mathematical function. However, ciphers that are secure when specified as mathematical functions are not necessarily secure in real-world implementations. The physical implementations of ciphers can be extremely difficult to control and often leak socalled side-channel information. Side-channel cryptanalysis attacks have shown to be especially effective as a practical means for attacking implementations of cryptographic algorithms on simple hardware platforms, such as smart-cards. Adversaries can obtain sensitive information from side-channels, such as the timing of operations, power consumption and electromagnetic emissions. Some of the attack techniques require surprisingly little side-channel information to break some of the best known ciphers. In constrained devices, such as smart-cards, straightforward implementations of cryptographic algorithms can be broken with minimal work. Preventing these attacks has become an active and a challenging area of research. Power analysis is a successful cryptanalytic technique that extracts secret information from cryptographic devices by analysing the power consumed during their operation. A particularly dangerous class of power analysis, differential power analysis (DPA), relies on the correlation of power consumption measurements. It has been proposed that adding non-determinism to the execution of the cryptographic device would reduce the danger of these attacks. It has also been demonstrated that asynchronous logic has advantages for security-sensitive applications. This thesis investigates the security and performance advantages of using a network-based asynchronous architecture, in which the functional units of the datapath form a network. Non-deterministic execution is achieved by exploiting concurrent execution of instructions both with and without data-dependencies; and by forwarding register values between instructions with data-dependencies using randomised routing over the network. The executions of cryptographic algorithms on different architectural configurations are simulated, and the obtained power traces are subjected to DPA attacks. The results show that the proposed architecture introduces a level of non-determinism in the execution that significantly raises the threshold for DPA attacks to succeed. In addition, the performance analysis shows that the improved security does not degrade performance

Distributed computation in computer networks

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