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Abstrak :
This book describes reliable and efficient design automation techniques for the design and implementation of an approximate computing system. The authors address the important facets of approximate computing hardware design - from formal verification and error guarantees to synthesis and test of approximation systems. They provide algorithms and methodologies based on classical formal verification, synthesis and test techniques for an approximate computing IC design flow. This is one of the first books in Approximate Computing that addresses the design automation aspects, aiming for not only sketching the possibility, but providing a comprehensive overview of different tasks and especially how they can be implemented.

Abstrak :
Di era modern yang dipenuhi dengan aplikasi-aplikasi berbasis data dan komputasi yang intensif sumber daya, Komputasi approximatif telah muncul sebagai pendekatan menjanjikan untuk menyeimbangkan antara akurasi dan efisiensi komputasi. Field Programmable Gate Arrays (FPGAs) menawarkan platform yang fleksibel untuk mengimplementasikan teknik tersebut, dan menjadikannya ideal untuk mengeksplorasi dampak komputasi approximatif dalam perangkat keras. Proyek ini mendalami penerapan teknik komputasi approximatif pada FPGA dalam konteks komputasi neuromorfik, dengan fokus pada implementasi spiking Izhikevich neuron model, model neuron yang banyak digunakan, dan mengevaluasi manfaat kinerja dan efisiensinya. Pada akhirnya, tesis ini bertujuan untuk memberikan informasi kepada para engineer dan designer tentang solusi komputasi yang lebih hemat energi dan responsif untuk masa depan. ......In the modern era of data-driven applications and resource-intensive computations, approximate computing has emerged as a promising approach to balance the trade-off between computational accuracy and efficiency. Field Programmable Gate Arrays (FPGAs) offer a flexible platform for implementing such techniques, making them ideal for exploring approximate computing in hardware. This project explores the application of approximate computing techniques on FPGAs in the context of neuromorphic computing, focusing on the implementation of the Izhikevich spiking neuron model, a widely used neural model, and evaluating its performance and efficiency benefits. Ultimately, the findings aim to inform engineers and designers of more energy-efficient and responsive computing solutions in the future.

Abstrak :
This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application, algorithmic, computer architecture, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy-applications, algorithms, hardware architectures, and circuits-supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realizations implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.

Abstrak :
This book covers the history and recent developments of stochastic computing. Stochastic computing (SC) was first introduced in the 1960s for logic circuit design, but its origin can be traced back to von Neumanns work on probabilistic logic. In SC, real numbers are encoded by random binary bit streams, and information is carried on the statistics of the binary streams. SC offers advantages such as hardware simplicity and fault tolerance. Its promise in data processing has been shown in applications including neural computation, decoding of error-correcting codes, image processing, spectral transforms and reliability analysis. There are three main parts to this book. The first part, comprising Chapters 1 and 2, provides a history of the technical developments in stochastic computing and a tutorial overview of the field for both novice and seasoned stochastic computing researchers. In the second part, comprising Chapters 3 to 8, we review both well-established and emerging design approaches for stochastic computing systems, with a focus on accuracy, correlation, sequence generation, and synthesis. The last part, comprising Chapters 9 and 10, provides insights into applications in machine learning and error-control coding.