標(biāo)題: Titlebook: Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing; Hardware Architectur Sudeep Pasricha,Muhammad Shafique Book 2024 The [打印本頁(yè)] 作者: CAP 時(shí)間: 2025-3-21 19:57
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing影響因子(影響力)
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing被引頻次
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing被引頻次學(xué)科排名
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing年度引用
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing年度引用學(xué)科排名
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing讀者反饋
書(shū)目名稱(chēng)Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing讀者反饋學(xué)科排名
作者: Foreshadow 時(shí)間: 2025-3-21 21:21
https://doi.org/10.1007/978-3-662-68073-5 neural networks can be accelerated in an energy-efficient manner. In particular, we focus on design considerations and trade-offs for mapping CNNs, Transformers, and GNNs on AI accelerators that attempt to maximize compute efficiency and minimize energy consumption by reducing the number of access to memory through efficient data reuse.作者: 遺傳 時(shí)間: 2025-3-22 03:22
https://doi.org/10.1007/978-3-8349-9996-2are. Thereafter, we discuss different interconnect techniques for IMC architectures proposed in the literature. Finally, different performance evaluation techniques for IMC architectures are described. We conclude the chapter with a summary and future avenues for IMC architectures for ML acceleration.作者: 陶醉 時(shí)間: 2025-3-22 06:02
Low- and Mixed-Precision Inference Acceleratorsd, all aiming at enabling neural network inference at the edge. In this chapter, design choices and their implications on the flexibility and energy efficiency of several accelerators supporting extremely quantized networks are reviewed.作者: filicide 時(shí)間: 2025-3-22 12:15 作者: 圓柱 時(shí)間: 2025-3-22 14:27
In-Memory Computing for AI Accelerators: Challenges and Solutionsare. Thereafter, we discuss different interconnect techniques for IMC architectures proposed in the literature. Finally, different performance evaluation techniques for IMC architectures are described. We conclude the chapter with a summary and future avenues for IMC architectures for ML acceleration.作者: 圓柱 時(shí)間: 2025-3-22 19:18 作者: 生命 時(shí)間: 2025-3-23 00:56 作者: Cryptic 時(shí)間: 2025-3-23 02:50 作者: 掙扎 時(shí)間: 2025-3-23 07:20
Embedded Machine Learning for Cyber-Physical, IoT, and Edge ComputingHardware Architectur作者: braggadocio 時(shí)間: 2025-3-23 10:22 作者: Innovative 時(shí)間: 2025-3-23 17:30 作者: 慢慢啃 時(shí)間: 2025-3-23 20:04
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.作者: 有限 時(shí)間: 2025-3-23 22:37
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.作者: 絆住 時(shí)間: 2025-3-24 04:10 作者: brachial-plexus 時(shí)間: 2025-3-24 07:49 作者: persistence 時(shí)間: 2025-3-24 10:40 作者: 母豬 時(shí)間: 2025-3-24 17:55 作者: 陪審團(tuán) 時(shí)間: 2025-3-24 21:55
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作者: Irremediable 時(shí)間: 2025-3-25 01:27
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作者: podiatrist 時(shí)間: 2025-3-25 04:00
Light Speed Machine Learning Inference on the Edgee level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, the proposed . architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN mode作者: MILL 時(shí)間: 2025-3-25 11:04 作者: 悲痛 時(shí)間: 2025-3-25 15:21
on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits..978-3-031-19570-9978-3-031-19568-6作者: 吵鬧 時(shí)間: 2025-3-25 19:52
https://doi.org/10.1007/978-3-662-32788-3output activations), thereby enabling our MPNA to operate under low power, while achieving high performance and energy efficiency. We synthesize our MPNA accelerator using the ASIC design flow for a 28-nm technology and perform functional and timing validation using real-world CNNs. Our MPNA achieve作者: 煩憂(yōu) 時(shí)間: 2025-3-25 23:20 作者: Accede 時(shí)間: 2025-3-26 01:00 作者: cochlea 時(shí)間: 2025-3-26 06:41
https://doi.org/10.1007/978-3-658-38198-1thodology employs an exploration technique to find the data partitioning and scheduling that offer minimum DRAM accesses for the given DNN model and exploits the low latency DRAMs to efficiently perform data accesses that incur minimum DRAM access energy.作者: 舊石器時(shí)代 時(shí)間: 2025-3-26 08:38
Meiofauna Sampling and Processing,rning (DL) applications by combining technology-specific circuit-level models and the actual memory behavior of various DL workloads. . relies on . and . performance and energy models for last-level caches implemented using conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the is作者: Capitulate 時(shí)間: 2025-3-26 16:13 作者: 生命 時(shí)間: 2025-3-26 18:14
The Earlier Cytological Investigations, DRAM. In: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)), Koppula S et al ((2019) EDEN: Enabling energy-efficient, high-performance deep neural network inference using approximate DRAM. arXiv), a recent work that uses this observation to realize higher作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-26 22:58 作者: Budget 時(shí)間: 2025-3-27 02:02
https://doi.org/10.1007/978-3-642-96347-6use 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作者: ELUDE 時(shí)間: 2025-3-27 08:11
Geschichtliche Perspektiven der Problemlage,e level, explore efficient corrective tuning for these devices, and integrate circuit-level optimization to counter thermal variations. As a result, the proposed . architecture possesses the desirable traits of being robust, energy-efficient, low latency, and high throughput, when executing BNN mode作者: 受傷 時(shí)間: 2025-3-27 13:22 作者: 蒼白 時(shí)間: 2025-3-27 17:09 作者: 錯(cuò)事 時(shí)間: 2025-3-27 20:24
https://doi.org/10.1007/978-3-663-02695-2paradigm has been explored to improve the energy-efficiency of silicon photonic networks-on-chip (PNoCs). Silicon photonic interconnects suffer from high power dissipation because of laser sources, which generate carrier wavelengths and tuning power required for regulating photonic devices under dif作者: 積習(xí)難改 時(shí)間: 2025-3-27 22:36 作者: 單獨(dú) 時(shí)間: 2025-3-28 04:20 作者: NUL 時(shí)間: 2025-3-28 08:12
https://doi.org/10.1007/978-3-662-68073-5alized accelerators to meet strict latency and energy constraints that are prevalent in both edge and cloud deployments. These accelerators achieve high performance through parallelism over hundreds of processing elements, and energy efficiency is achieved by reducing data movement and maximizing re作者: ANNUL 時(shí)間: 2025-3-28 12:06
https://doi.org/10.1007/978-3-658-38198-1e a prominent solution for many machine learning (ML) tasks, like personalized healthcare assistance. Such implementations require high energy efficiency since embedded applications usually have tight operational constraints, such as small memory and low operational power/energy. Therefore, speciali作者: ENACT 時(shí)間: 2025-3-28 16:41
https://doi.org/10.1007/978-3-8349-9996-2date, several SRAM/ReRAM-based IMC hardware architectures to accelerate ML applications have been proposed in the literature. However, crossbar-based IMC hardware poses several design challenges. In this chapter, we first describe different machine learning algorithms adopted in the literature recen作者: 獸皮 時(shí)間: 2025-3-28 19:04
Meiofauna Sampling and Processing,tance for training ML models. With this comes the challenge of overall efficient deployment, in particular low-power and high-throughput implementations, under stringent memory constraints. In this context, non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access me作者: mechanism 時(shí)間: 2025-3-28 23:09 作者: podiatrist 時(shí)間: 2025-3-29 05:45
The Earlier Cytological Investigations,he increasing memory intensity of most DNN workloads, main memory can dominate the system’s energy consumption and stall time. One effective way to reduce the energy consumption and increase the performance of DNN inference systems is by using approximate memory, which operates with reduced supply v作者: 紅腫 時(shí)間: 2025-3-29 08:17 作者: 字形刻痕 時(shí)間: 2025-3-29 13:18 作者: 中止 時(shí)間: 2025-3-29 19:37
Geschichtliche Perspektiven der Problemlage,CPUs and GPUs. Such accelerators are thus well suited for resource-constrained embedded systems. However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead. Binarized neural networks (BNN作者: apropos 時(shí)間: 2025-3-29 22:59 作者: Incisor 時(shí)間: 2025-3-30 02:57
https://doi.org/10.1007/978-3-031-19568-6Machine learning embedded systems; Machine learning IoT; Machine learning edge computing; Smart Cyber-P作者: milligram 時(shí)間: 2025-3-30 07:32
978-3-031-19570-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 不近人情 時(shí)間: 2025-3-30 08:23 作者: Enervate 時(shí)間: 2025-3-30 12:44
