12 accepted papers
3D Gaussian Splatting (3DGS) has become a crucial rendering technique for many real-time applications. How- ever, the limited hardware resources on today's mobile platforms hinder these applications, as they struggle to achieve real-time performance. In this paper, we propose SEELE, a general framew
Visual streaming perception (VSP) involves online intelligent processing of sequential frames captured by vision sensors, enabling real-time decision-making in applications such as autonomous driving, UAVs, and AR/VR. However, the computational efficiency of VSP on edge devices remains a challenge d…
Jacobi decoding shows promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into more parallelizable computation. However, in practice, it achieves little speedup compared to traditional autoregressive (AR) decoding, primarily becau…
Spiking neural network (SNN) has attracted great attention due to its characteristic of high efficiency and accuracy. Currently, the ANN-to-SNN conversion methods can obtain ANN on-par accuracy SNN with ultra-low latency (8 time-steps) in CNN structure on computer vision (CV) tasks. However, as Tran…
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. While such a scheme helps reduce the computational l…
Model quantization has emerged as a mandatory technique for efficient inference with advanced Deep Neural Networks (DNN). It converts the model parameters in full precision (32-bit floating point) to the hardware friendly data representation with shorter bit-width, to not only reduce the model size…
Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, and this is known as catastrophic forgetting. To learn new task without forgetting, recently, the mask-based learning method (e.g. piggyback ) is proposed to address these issues by learning only a bin…
Recently, a new paradigm of the adversarial attack on the quantized neural network weights has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka. Bit-Flip Attack (BFA). BFA has shown extraordinary attacking ability, where the adversary can malfunction a quantized D…
Security of modern Deep Neural Networks (DNNs) is under severe scrutiny as the deployment of these models become widespread in many intelligence-based applications. Most recently, DNNs are attacked through Trojan which can effectively infect the model during the training phase and get activated only…
Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated security concern of DNN is from its malicious input, a.k.a adversarial example. Nevertheless, the security challenge of DNN's p…
Recent developments in the field of Deep Learning have exposed the underlying vulnerability of Deep Neural Network (DNN) against adversarial examples. In image classification, an adversarial example is a carefully modified image that is visually imperceptible to the original image but can cause DNN…
In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource-limited mobi…