Measuring NPU Performance - Edge AI and Vision Alliance
Synopsys' ARC Embedded Vision Processors Delivers Industry-Leading 35 TOPS Performance for AI | Maker Pro
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
Mipsology Zebra on Xilinx FPGA Beats GPUs, ASICs for ML Inference Efficiency - Embedded Computing Design
TOPS, Memory, Throughput And Inference Efficiency
Not all TOPs are created equal. Deep Learning processor companies often… | by Forrest Iandola | Analytics Vidhya | Medium
A 0.32–128 TOPS, Scalable Multi-Chip-Module-Based Deep Neural Network Inference Accelerator With Ground-Referenced Signaling in 16 nm | Research
Electronics | Free Full-Text | Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey
As AI chips improve, is TOPS the best way to measure their power? | VentureBeat
Imagination Announces First PowerVR Series2NX Neural Network Accelerator Cores: AX2185 and AX2145
![VLSI 2018] A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural- Network Processor with Cell Current Controlled Writing and Flexible Network Architecture](https://t1.daumcdn.net/cfile/tistory/993DBA3E5BAF92CE38 "VLSI 2018] A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural- Network Processor with Cell Current Controlled Writing and Flexible Network Architecture")
VLSI 2018] A 4M Synapses integrated Analog ReRAM based 66.5 TOPS/W Neural- Network Processor with Cell Current Controlled Writing and Flexible Network Architecture
Hailo-8™ AI Processor For Edge Devices | Up to 26 Tops Hardware
Atomic, Molecular, and Optical Physics | Department of Physics | City University of Hong Kong
VeriSilicon Launches VIP9000, New Generation of Neural Processor Unit IP | Markets Insider
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
Micro-combs enable 11 TOPS photonic convolutional neural networ...
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks - Xilinx & Numenta
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
As AI chips improve, is TOPS the best way to measure their power? | VentureBeat
A 1.32 TOPS/W Energy Efficient Deep Neural Network Learning Processor with Direct Feedback Alignment based Heterogeneous Core Architecture | Semantic Scholar
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural networks - Xilinx & Numenta
A 17–95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm | Research
