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Titlebook: Approximate Circuits; Methodologies and CA Sherief Reda,Muhammad Shafique Book 2019 Springer Nature Switzerland AG 2019 approximate by desi

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51#
發(fā)表于 2025-3-30 10:42:25 | 只看該作者
52#
發(fā)表于 2025-3-30 15:18:53 | 只看該作者
Approximate High-Level Synthesis of Custom Hardwareware approximations strongly depend on input data. In this work, a statistical formulation is employed to capture input dependency and analytically estimate quality using one-time profiling only. Energy and performance savings due to approximations strongly depend on operation scheduling and binding
53#
發(fā)表于 2025-3-30 16:59:32 | 只看該作者
54#
發(fā)表于 2025-3-31 00:18:48 | 只看該作者
Hardware–Software Approximations for Deep Neural Networksr and precision reduction and memory approximations at the hardware layer. Towards the end of this chapter, we also highlight few of the prominent challenges in adopting different types of approximation and the effects that they have on the overall efficiency and accuracy of the baseline networks.
55#
發(fā)表于 2025-3-31 04:00:26 | 只看該作者
Lightweight Deep Neural Network Accelerators Using Approximate SW/HW Techniquesng techniques that use an ensemble of lightweight DNN accelerators to achieve the same or better accuracy than the original floating-point accelerator, while still using much less hardware resources. Using 65 nm technology libraries and industrial-strength design flow, we demonstrate a custom hardwa
56#
發(fā)表于 2025-3-31 05:52:43 | 只看該作者
Approximate Computing for Iris Recognition Systemsch typically target individual algorithms, this chapter explores a complex software/hardware pipeline system for iris code computation from live camera feed using an FPGA-based SoC. Our flow consists of four major algorithms, through which eight approximation knobs are identified for accuracy versus
57#
發(fā)表于 2025-3-31 10:59:18 | 只看該作者
58#
發(fā)表于 2025-3-31 15:45:04 | 只看該作者
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