標(biāo)題: Titlebook: Embedded Deep Learning; Algorithms, Architec Bert Moons,Daniel Bankman,Marian Verhelst Book 2019 Springer Nature Switzerland AG 2019 Deep L [打印本頁(yè)] 作者: FLAW 時(shí)間: 2025-3-21 18:00
書(shū)目名稱(chēng)Embedded Deep Learning影響因子(影響力)
書(shū)目名稱(chēng)Embedded Deep Learning影響因子(影響力)學(xué)科排名
書(shū)目名稱(chēng)Embedded Deep Learning網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱(chēng)Embedded Deep Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Embedded Deep Learning被引頻次
書(shū)目名稱(chēng)Embedded Deep Learning被引頻次學(xué)科排名
書(shū)目名稱(chēng)Embedded Deep Learning年度引用
書(shū)目名稱(chēng)Embedded Deep Learning年度引用學(xué)科排名
書(shū)目名稱(chēng)Embedded Deep Learning讀者反饋
書(shū)目名稱(chēng)Embedded Deep Learning讀者反饋學(xué)科排名
作者: gnarled 時(shí)間: 2025-3-21 23:24
Optimized Hierarchical Cascaded Processing,discusses a first . solution for this problem. In this chapter, the wake-up-based detection scenario is generalized to ., where a hierarchy of increasingly complex classifiers, each designed and trained for a specific sub-task, is used to minimize the overall system’s energy cost. An optimal hierarc作者: 隱語(yǔ) 時(shí)間: 2025-3-22 03:14
Hardware-Algorithm Co-optimizations,discusses hardware aware . solutions for this problem. As an introduction to this topic, this chapter gives an overview of existing work in hardware and neural network co-optimizations. Two own contributions in hardware-algorithm optimization are discussed and compared: network quantization either a作者: Nonflammable 時(shí)間: 2025-3-22 04:42 作者: stress-response 時(shí)間: 2025-3-22 11:01 作者: 隨意 時(shí)間: 2025-3-22 13:10 作者: 隨意 時(shí)間: 2025-3-22 18:21
Conclusions, Contributions, and Future Work,ained wearable edge devices. Although SotA in many typical machine-learning tasks, deep learning algorithms are also very costly in terms of energy consumption, due to their large amount of required computations and huge model sizes. Because of this, deep learning applications on battery-constrained作者: Ovulation 時(shí)間: 2025-3-22 21:52 作者: 美麗的寫(xiě) 時(shí)間: 2025-3-23 01:57 作者: 易怒 時(shí)間: 2025-3-23 08:29 作者: 1分開(kāi) 時(shí)間: 2025-3-23 10:16
978-3-030-07577-4Springer Nature Switzerland AG 2019作者: 可觸知 時(shí)間: 2025-3-23 15:54 作者: Genome 時(shí)間: 2025-3-23 19:59
L. F. Clausdorff,K. -P. Hoffmanndiscusses a first . solution for this problem. In this chapter, the wake-up-based detection scenario is generalized to ., where a hierarchy of increasingly complex classifiers, each designed and trained for a specific sub-task, is used to minimize the overall system’s energy cost. An optimal hierarc作者: genesis 時(shí)間: 2025-3-23 23:04
https://doi.org/10.1007/978-3-662-12453-6discusses hardware aware . solutions for this problem. As an introduction to this topic, this chapter gives an overview of existing work in hardware and neural network co-optimizations. Two own contributions in hardware-algorithm optimization are discussed and compared: network quantization either a作者: transplantation 時(shí)間: 2025-3-24 04:10 作者: semiskilled 時(shí)間: 2025-3-24 09:25
B. Shah-Derler,E. Wintermantel,S. -W. Hacy through leveraging the three key CNN-characteristics discussed in Chap. .. (a) Inherent CNN parallelism is exploited through a highly parallelized processor architecture that minimizes internal memory bandwidth. (b) Inherent network sparsity in pruned networks and RELU activated feature maps is l作者: 天然熱噴泉 時(shí)間: 2025-3-24 12:15 作者: 誘拐 時(shí)間: 2025-3-24 18:39 作者: 嚴(yán)厲批評(píng) 時(shí)間: 2025-3-24 21:04
L. F. Clausdorff,K. -P. Hoffmannes in a 100-face recognition example. The chips designed in Chap. . are specifically tuned for usage in a hierarchical setup: networks at reduced precision can be used for simple tasks at a high energy efficiency. The chips designed in Chap. . are good candidates for wake-up stages.作者: ALIBI 時(shí)間: 2025-3-24 23:30 作者: deface 時(shí)間: 2025-3-25 04:07 作者: 舔食 時(shí)間: 2025-3-25 08:50
oncepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts..978-3-030-07577-4978-3-319-99223-5作者: N斯巴達(dá)人 時(shí)間: 2025-3-25 13:14
Optimized Hierarchical Cascaded Processing,es in a 100-face recognition example. The chips designed in Chap. . are specifically tuned for usage in a hierarchical setup: networks at reduced precision can be used for simple tasks at a high energy efficiency. The chips designed in Chap. . are good candidates for wake-up stages.作者: 遺傳學(xué) 時(shí)間: 2025-3-25 17:37
Circuit Techniques for Approximate Computing, third major contribution of this text. It is a dynamic arithmetic precision scaling method on the circuit-level that enables minimum energy test-time FPNNs and QNNs, as discussed in Chap. .. Chapter . discusses two physically implemented CNN chips that apply this DVAFS technique in real silicon. Bi作者: Apoptosis 時(shí)間: 2025-3-25 23:08
Conclusions, Contributions, and Future Work,feasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, this wireless connection is very inefficient—requiring too much energy per transferred bit for real-time 作者: TATE 時(shí)間: 2025-3-26 02:07 作者: 音樂(lè)會(huì) 時(shí)間: 2025-3-26 08:02
Gebrauchstauglichkeit von Medizinproduktenthis chapter lists the challenges associated with the large compute requirements in deep learning and outlines a vision to overcome them. Finally, this chapter gives an overview of my contributions to the field and a general structure of the book.作者: Aboveboard 時(shí)間: 2025-3-26 10:34 作者: 運(yùn)動(dòng)的我 時(shí)間: 2025-3-26 14:16 作者: tariff 時(shí)間: 2025-3-26 19:50
Book 2019es 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 overvie作者: dermatomyositis 時(shí)間: 2025-3-27 00:21 作者: 自戀 時(shí)間: 2025-3-27 02:54
B. Shah-Derler,E. Wintermantel,S. -W. Hanimizing energy consumption for any CNN, with any computational precision requirement up to 16b fixed-point. Through its energy-scalability and high energy-efficiency, Envision lends itself perfectly for hierarchical applications, discussed in Chap. .. It hereby enables CNN processing in always-on embedded applications.作者: 特別容易碎 時(shí)間: 2025-3-27 06:30 作者: Oration 時(shí)間: 2025-3-27 13:28
ENVISION: Energy-Scalable Sparse Convolutional Neural Network Processing,nimizing energy consumption for any CNN, with any computational precision requirement up to 16b fixed-point. Through its energy-scalability and high energy-efficiency, Envision lends itself perfectly for hierarchical applications, discussed in Chap. .. It hereby enables CNN processing in always-on embedded applications.作者: 騷動(dòng) 時(shí)間: 2025-3-27 15:03
BINAREYE: Digital and Mixed-Signal Always-On Binary Neural Network Processing,n implementation of the . architecture, focuses on the system level. It is designed for flexibility, allowing it to trade-off energy for network accuracy at run-time. This chapter discuses and compares both designs.作者: 有發(fā)明天才 時(shí)間: 2025-3-27 19:43
ses the optimization of neural networks for embedded deploym.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 作者: 親密 時(shí)間: 2025-3-28 00:10
Multibody and Macroscopic Impact Laws: A Convex Analysis Standpoint,ing macroscopic impact models more respectful of the underlying microscopic structure, in particular we establish micro-macro convergence results under strong assumptions on the microscopic structure.作者: crescendo 時(shí)間: 2025-3-28 04:01
Thomas Richter show that the live synthesis problem can be solved within the same complexity bound as standard reactive synthesis, i.e., in 2EXPTIME. Our experiments show the necessity of live synthesis for LiveLTL specifications created from benchmarks of SYNTCOMP and robot control.作者: 宣稱(chēng) 時(shí)間: 2025-3-28 06:41 作者: 改革運(yùn)動(dòng) 時(shí)間: 2025-3-28 12:30
Emil Ni??,Daniel Comeagarstand their positive and negative effect on the creative design process. An overview of the main influences and opportunities collected by adopting the two tools are presented with guidelines to design actions to empower the process for innovation..978-3-030-87260-1978-3-030-87258-8Series ISSN 2661-8184 Series E-ISSN 2661-8192 作者: Favorable 時(shí)間: 2025-3-28 18:17 作者: insert 時(shí)間: 2025-3-28 22:07 作者: 反應(yīng) 時(shí)間: 2025-3-29 02:29
A Survey of Contributions to Fuzzy Logic and Its Applications to Artificial Intelligence at the IIIood balance between basic research and applications, and paying particular attention to training PhD students and technology transfer. In this article, we survey some of the most relevant results related to Fuzzy Logic and Fuzzy AI Systems that we have obtained since the initiation of our research activities in 1985.
