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Found 20 papers, 5 papers with code
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML.
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate.
Accelerating Attention through Gradient-Based Learned Runtime Pruning
To best utilize this mathematical innovation, we devise a bit-serial architecture, dubbed LeOPArd, for transformer language models with bit-level early termination microarchitectural mechanism.
Conscious AI
Recent advances in artificial intelligence (AI) have achieved human-scale speed and accuracy for classification tasks.
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION
We show how WaveQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
Privacy in Deep Learning: A Survey
In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.
Bit-Parallel Vector Composability for Neural Acceleration
This paper explores a different design style, where each unit is only responsible for a slice of the bit-level operations to interleave and combine the benefits of bit-level parallelism with the abundant data-level parallelism in deep neural networks.
Not All Features Are Equal: Discovering Essential Features for Preserving Prediction Privacy
We formulate this problem as a gradient-based perturbation maximization method that discovers this subset in the input feature space with respect to the functionality of the prediction model used by the provider.
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices
To address this standing issue, we present a memory-aware compiler, dubbed SERENITY, that utilizes dynamic programming to find a sequence that finds a schedule with optimal memory footprint.
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
We show how SINAREQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself.
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs).
Hardware Architecture
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network.
Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation
Further experiments also confirm that our adaptive sampling can even improve AutoTVM's simulated annealing by 4. 00x.
Shredder: Learning Noise Distributions to Protect Inference Privacy
To address this challenge, this paper devises Shredder, an end-to-end framework, that, without altering the topology or the weights of a pre-trained network, learns additive noise distributions that significantly reduce the information content of communicated data while maintaining the inference accuracy.
SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training
To further mitigate this loss, we propose a novel sinusoidal regularization, called SinReQ1, for deep quantized training.
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
We show how ReLeQ can balance speed and quality, and provide an asymmetric general solution for quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet, MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy (=< 0. 3% loss) while minimizing the computation and storage cost.
GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks
Even though there is a convolution stage in this operator, the inserted zeros lead to underutilization of the compute resources when a conventional convolution accelerator is employed.
In-RDBMS Hardware Acceleration of Advanced Analytics
The data revolution is fueled by advances in machine learning, databases, and hardware design.
Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Networks
Compared to Stripes, BitFusion provides 2. 6x speedup and 3. 9x energy reduction at 45 nm node when BitFusion area and frequency are set to those of Stripes.
