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Found 19 papers, 5 papers with code
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
The resultant models, denoted as MobileLLM-LS, demonstrate a further accuracy enhancement of 0. 7%/0. 8% than MobileLLM 125M/350M.
Not All Weights Are Created Equal: Enhancing Energy Efficiency in On-Device Streaming Speech Recognition
This study delves into how weight parameters in speech recognition models influence the overall power consumption of these models.
Folding Attention: Memory and Power Optimization for On-Device Transformer-based Streaming Speech Recognition
Instead, the bottleneck lies in the linear projection layers of multi-head attention and feedforward networks, constituting a substantial portion of the model size and contributing significantly to computation, memory, and power usage.
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads
Such dynamic behaviors introduce new challenges to the system software in an ML system since the overall system load is not completely predictable, unlike traditional ML workloads.
XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse
We hope that our work will stimulate research and lead to the development of a new generation of ML systems for XR use cases.
Low-Rank+Sparse Tensor Compression for Neural Networks
Low-rank tensor compression has been proposed as a promising approach to reduce the memory and compute requirements of neural networks for their deployment on edge devices.
Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation
Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs.
Ranked #24 on
Semantic Segmentation
on Cityscapes val
Improving Efficiency in Neural Network Accelerator Using Operands Hamming Distance optimization
Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric.
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs).
Heterogeneous Dataflow Accelerators for Multi-DNN Workloads
The results suggest that HDA is an alternative class of Pareto-optimal accelerators to RDA with strength in energy, which can be a better choice than RDAs depending on the use cases.
Distributed, Parallel, and Cluster Computing
Rethinking Machine Learning Development and Deployment for Edge Devices
Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks.
Federated Learning with Non-IID Data
Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
Deep Neural Networks are becoming increasingly popular in always-on IoT edge devices performing data analytics right at the source, reducing latency as well as energy consumption for data communication.
Not All Ops Are Created Equal!
Efficient and compact neural network models are essential for enabling the deployment on mobile and embedded devices.
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.
Hello Edge: Keyword Spotting on Microcontrollers
We train various neural network architectures for keyword spotting published in literature to compare their accuracy and memory/compute requirements.
Ranked #13 on
Keyword Spotting
on Google Speech Commands
PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training
Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints.
Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
To alleviate these problems to some extent, prior research utilize low precision fixed-point numbers to represent the CNN weights and activations.
