… For accelerators, the optimization objective of this work is to design a high energy efficient
accelerator that can adapt to various network layers and dataflow patterns. The bridge …

… accelerating deep learning networks on FPGAs. We highlight the key features employed by
the various techniques for improving the acceleration … of FPGAs for CNNs acceleration. The …

… acceleration. To realize this dynamic reconfigurability, we first devise breakable omni-directional
systolic arrays for DNN acceleration … even allows breaking the accelerator with regard to …

… layer, so that the network has a certain degree of adaptability to the local transformation of
the image. The pooling layer has the ability to compress features, reduce the complexity of the …

… , we survey hardware-accelerated infrastructures that connect GPC platforms to networks
(eg, smart network interface cards). We find that the CPU hardware accelerations have mainly …

… ABSTRACT | Successful integration of deep neural networks (DNNs) or deep learning (DL)
has resulted in breakthroughs in many areas. However, deploying these highly accurate …

… Our design, called SNNAP (systolic neural network accelerator in programmable logic),
is designed to work with a compiler work ow that automatically con gures the neural network’s …

… The IMU sensor is ADIS16448, which senses acceleration … order of time, 3-axis acceleration,
3 gyroscope measurements, 3 … a neural network for unsupervised deep learning method for …

… the unique challenges of deep learning acceleration in IoT environments. … learning
accelerators holds significant promise for advancing the efficiency and adaptability of neural network …

… adaptability to IoT devices under changing circumstances. STICKER supports both … Thus,
the quantization center-based circuit accelerates weight updating by 5 orders of magnitude. 2) …