Search Results for author:
Found 13 papers, 3 papers with code
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360deg
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360deg with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm
To this end, we propose a novel MORL algorithm that trains a single universal network to cover the entire preference space scalable to continuous robotic tasks.
Continuous Control
Multi-Objective Reinforcement Learning
+1
COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks
Besides accelerating the computation using custom compute elements (CE) and in-memory computing, COIN aims at minimizing the intra- and inter-CE communication in GCN operations to optimize the performance and energy efficiency.
Fast and Scalable Human Pose Estimation using mmWave Point Cloud
Millimeter-Wave (mmWave) radar can enable high-resolution human pose estimation with low cost and computational requirements.
tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices
Optimal energy management is a challenging task due to the dynamic nature of the harvested energy and the battery energy constraints of the target device.
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic challenges on area, yield, and on-chip interconnection cost due to the ever-increasing model sizes.
FLASH: Fast Neural Architecture Search with Hardware Optimization
This paper proposes FLASH, a very fast NAS methodology that co-optimizes the DNN accuracy and performance on a real hardware platform.
Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks
In this technique, we use analytical models of NoC to evaluate end-to-end communication latency of any given DNN.
Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices
The proposed approach decreases the hypervector dimension by more than $32\times$ while maintaining or increasing the accuracy achieved by conventional HDC.
Online Adaptive Learning for Runtime Resource Management of Heterogeneous SoCs
Dynamic resource management has become one of the major areas of research in modern computer and communication system design due to lower power consumption and higher performance demands.
User-Space Emulation Framework for Domain-Specific SoC Design
In this work, we propose a portable, Linux-based emulation framework to provide an ecosystem for hardware-software co-design of Domain-specific SoCs (DSSoCs) and enable their rapid evaluation during the pre-silicon design phase.
Distributed, Parallel, and Cluster Computing
An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms
To address this need, system-on-chips (SoC) that are at the heart of these devices provide a variety of control knobs, such as the number of active cores and their voltage/frequency levels.
Online Human Activity Recognition using Low-Power Wearable Devices
Recent research on HAR focuses on using smartphones due to their widespread use.
