Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Sai-Qian Zhang"'
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031198083
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad5a2b0c7d2b3be99a20576232f4dfb0
https://doi.org/10.1007/978-3-031-19809-0_10
https://doi.org/10.1007/978-3-031-19809-0_10
Publikováno v:
ISCAS
The proposed saturation RRAM for in-memory computing of a pre-trained Convolutional Neural Network (CNN) inference imposes a limit on the maximum analog value output from each bitline in order to reduce analog-to-digital (A/D) conversion costs. The p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0ac27a662d5a86c7d7b5d3c8574cb66
https://doi.org/10.1109/iscas51556.2021.9401293
https://doi.org/10.1109/iscas51556.2021.9401293
Publikováno v:
ASPLOS
Low-resolution uniform quantization (e.g., 4-bit bitwidth) for both Deep Neural Network (DNN) weights and data has emerged as an important technique for efficient inference. Departing from conventional quantization, we describe a novel training appro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28b61d4bf1aba41e69b89cc2a621e8cb
https://doi.org/10.1145/3445814.3446741
https://doi.org/10.1145/3445814.3446741
Block Floating Point (BFP) can efficiently support quantization for Deep Neural Network (DNN) training by providing a wide dynamic range via a shared exponent across a group of values. In this paper, we propose a Fast First, Accurate Second Training
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f377e704e0e57ada3dd88b219c076af5
Publikováno v:
SC
We present a novel technique, called Term Quantization (TQ), for furthering quantization at run time for improved computational efficiency of deep neural networks (DNNs) already quantized with conventional quantization methods. TQ operates on power-o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6eeec9654e4a2f4a41f76ffeaffe88a
https://doi.org/10.1109/sc41405.2020.00100
https://doi.org/10.1109/sc41405.2020.00100
Publikováno v:
ICPP
The emergence of the Internet of Things (IoT) has led to a remarkable increase in the volume of data generated at the network edge. In order to support real-time smart IoT applications, massive amounts of data generated from edge devices need to be p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03aa5ab14b7139b4362412e5bc151194
https://doi.org/10.1145/3404397.3404473
https://doi.org/10.1145/3404397.3404473
Publikováno v:
SP Workshops
In cooperative multi-agent reinforcement learning (c-MARL), agents learn to cooperatively take actions as a team to maximize a total team reward. We analyze the robustness of c-MARL to adversaries capable of attacking one of the agents on a team. Thr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab20d91f9892fb89fa7f6541d20cc8d4
https://doi.org/10.1109/spw50608.2020.00027
https://doi.org/10.1109/spw50608.2020.00027
Publikováno v:
AAAI
To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed-up, memory saving with quantized activation and weights. We first bri
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d96ce211e6e7cf87d489c48a05c55f56
http://arxiv.org/abs/1912.02057
http://arxiv.org/abs/1912.02057
Autor:
Ali Tizghadam, Hadi Bannazadeh, Sai Qian Zhang, Qi Zhang, Raouf Boutaba, Byungchul Park, Alberto Leon-Garcia
Publikováno v:
Computer Networks. 125:26-40
Software Defined Networking (SDN) enables centralized control over distributed network resources. In SDN, a central controller can achieve fine-grained control over individual flows by installing appropriate forwarding rules in the network. This allo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2d06ac111282a224766e0a08e1c2d8f
https://doi.org/10.1016/j.comnet.2017.06.020
https://doi.org/10.1016/j.comnet.2017.06.020
Publikováno v:
ASAP
We present the Maestro memory-on-logic 3D-IC architecture for coordinated parallel use of a plurality of systolic arrays (SAs) in performing deep neural network (DNN) inference. Maestro reduces under-utilization common for a single large SA by allowi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9a4c1382ead4098dd1fb00ec92a8681
https://doi.org/10.1109/asap.2019.00-31
https://doi.org/10.1109/asap.2019.00-31