Design principles for lifelong learning AI accelerators
Lifelong learning—an agent's ability to learn throughout its lifetime—is a hallmark of
biological learning systems and a central challenge for artificial intelligence (AI). The …
biological learning systems and a central challenge for artificial intelligence (AI). The …
An overview of energy-efficient hardware accelerators for on-device deep-neural-network training
J Lee, HJ Yoo - IEEE Open Journal of the Solid-State Circuits …, 2021 - ieeexplore.ieee.org
Deep Neural Networks (DNNs) have been widely used in various artificial intelligence (AI)
applications due to their overwhelming performance. Furthermore, recently, several …
applications due to their overwhelming performance. Furthermore, recently, several …
9.2 A 28nm 12.1 TOPS/W dual-mode CNN processor using effective-weight-based convolution and error-compensation-based prediction
To deploy convolutional neural networks (CNNs) on edge devices efficiently, most existing
CNN processors were built on quantized CNNs to optimize the inference operations …
CNN processors were built on quantized CNNs to optimize the inference operations …
CHIMERA: A 0.92-TOPS, 2.2-TOPS/W edge AI accelerator with 2-MByte on-chip foundry resistive RAM for efficient training and inference
Implementing edge artificial intelligence (AI) inference and training is challenging with
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …
current memory technologies. As deep neural networks (DNNs) grow in size, this problem is …
7.4 GANPU: A 135TFLOPS/W multi-DNN training processor for GANs with speculative dual-sparsity exploitation
Generative adversarial networks (GAN) have a wide range of applications, from image style
transfer to synthetic voice generation [1]. GAN applications on mobile devices, such as face …
transfer to synthetic voice generation [1]. GAN applications on mobile devices, such as face …
9.3 a 40nm 4.81 TFLOPS/W 8b floating-point training processor for non-sparse neural networks using shared exponent bias and 24-way fused multiply-add tree
Recent works on mobile deep-learning processors have presented designs that exploit
sparsity [2, 3], which is commonly found in various neural networks. However, due to the shift …
sparsity [2, 3], which is commonly found in various neural networks. However, due to the shift …
Direct feedback alignment with sparse connections for local learning
Recent advances in deep neural networks (DNNs) owe their success to training algorithms
that use backpropagation and gradient-descent. Backpropagation, while highly effective on …
that use backpropagation and gradient-descent. Backpropagation, while highly effective on …
A 65-nm neuromorphic image classification processor with energy-efficient training through direct spike-only feedback
Recent advances in neural network (NN) and machine learning algorithms have sparked a
wide array of research in specialized hardware, ranging from high-performance NN …
wide array of research in specialized hardware, ranging from high-performance NN …
GANPU: An energy-efficient multi-DNN training processor for GANs with speculative dual-sparsity exploitation
This article presents generative adversarial network processing unit (GANPU), an energy-
efficient multiple deep neural network (DNN) training processor for GANs. It enables on …
efficient multiple deep neural network (DNN) training processor for GANs. It enables on …
A neural network training processor with 8-bit shared exponent bias floating point and multiple-way fused multiply-add trees
Recent advances in deep neural networks (DNNs) and machine learning algorithms have
induced the demand for services based on machine learning algorithms that require a large …
induced the demand for services based on machine learning algorithms that require a large …