Titlebook: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing; Hardware Architectur Sudeep Pasricha,Muhammad Shafique Book 2024 The

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Innovative

慢慢啃

Photonic NoCs for Energy-Efficient Data-Centric Computing framework can achieve up to 56.4% lower laser-power consumption and up to 23.8% better energy-efficiency than the best-known prior work on approximate communication with silicon photonic interconnects and for the same application output quality.

有限

Designing Resource-Efficient Hardware Arithmetic for FPGA-Based Accelerators Leveraging Approximatioods and delve into the details of selected works that report considerable improvements in this regard. Specifically, we cover custom optimizations for both accurate and approximate multiplier designs and MAC units employing mixed quantization of Posit and fixed-point/integer number representations.

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persistence

母豬

陪審團(tuán)

On-Chip DNN Training for Direct Feedback Alignment in FeFETer improve the power efficiency, we identify two architectural challenges unique to DFA-based training: a low-cost on-chip random number generator and an efficient analog-to-digital converter (ADC). We then propose a random number generator based on the statistical switching in FeFETs and an ultra-l

Irremediable

Platform-Based Design of Embedded Neuromorphic Systemsuse of the system software for many different hardware platforms. We describe how platform-based design methodologies can be applied to neuromorphic system design. Specifically, we show that a given system software framework can be optimized to achieve performance, energy, and reliability goals of a