11 TOPS photonic convolutional accelerator for optical neural networks | Nature
A List of Chip/IP for Deep Learning | by Shan Tang | Medium
TOPS, Memory, Throughput And Inference Efficiency
Figure 5 from Sticker: A 0.41-62.1 TOPS/W 8Bit Neural Network Processor with Multi-Sparsity Compatible Convolution Arrays and Online Tuning Acceleration for Fully Connected Layers | Semantic Scholar
Mipsology Zebra on Xilinx FPGA Beats GPUs, ASICs for ML Inference Efficiency - Embedded Computing Design
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
Not all TOPs are created equal. Deep Learning processor companies often… | by Forrest Iandola | Analytics Vidhya | Medium
FPGA Conference 2021: Breaking the TOPS ceiling with sparse neural ne…
PDF) BRein Memory: A Single-Chip Binary/Ternary Reconfigurable in-Memory Deep Neural Network Accelerator Achieving 1.4 TOPS at 0.6 W
Measuring NPU Performance - Edge AI and Vision Alliance
TOPS: The Truth Behind a Deep Learning Lie - EE Times
Are Tera Operations Per Second (TOPS) Just hype? Or Dark AI Silicon in Disguise? - KDnuggets
Hailo-8 26-TOPS Neural Accelerator M.2 A+E 2230 – JeVois Smart Machine Vision
Figure 1 from A 3.43TOPS/W 48.9pJ/pixel 50.1nJ/classification 512 analog neuron sparse coding neural network with on-chip learning and classification in 40nm CMOS | Semantic Scholar
Figure 4 from A 44.1TOPS/W Precision-Scalable Accelerator for Quantized Neural Networks in 28nm CMOS | Semantic Scholar
TOPS, Memory, Throughput And Inference Efficiency
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
When “TOPS” are Misleading. Neural accelerators are often… | by Jan Werth | Towards Data Science
Sparsity engine boost for neural network IP core ...
TOPS, Memory, Throughput And Inference Efficiency
11 TOPS photonic convolutional accelerator for optical neural networks | Nature
A 161.6 TOPS/W Mixed-mode Computing-in-Memory Processor for Energy-Efficient Mixed-Precision Deep Neural Networks (유회준교수 연구실) - KAIST 전기 및 전자공학부
TOPS, Memory, Throughput And Inference Efficiency
TOPS, Memory, Throughput And Inference Efficiency
