Optimization of Microarchitecture and Dataflow for Sparse Tensor CNN Acceleration by Ngoc-Son Pham, Taeweon Suh

Subjects: “…AI accelerator…”
Get full text

StarSPA: Stride-Aware Sparsity Compression for Efficient CNN Acceleration by Ngoc-Son Pham, Sangwon Shin, Lei Xu, Weidong Shi, Taeweon Suh

Subjects: “…AI accelerator…”
Get full text

SENECA: building a fully digital neuromorphic processor, design trade-offs and challenges by Guangzhi Tang, Kanishkan Vadivel, Yingfu Xu, Refik Bilgic, Kevin Shidqi, Paul Detterer, Stefano Traferro, Mario Konijnenburg, Manolis Sifalakis, Gert-Jan van Schaik, Amirreza Yousefzadeh

Subjects: Get full text

Analysis and Enhancement of Resilience for LSTM Accelerators Using Residue-Based CEDs by Nooshin Nosrati, Zainalabedin Navabi

Subjects: “…AI accelerators…”
Get full text

Modeling and Library Support for Early-Stage Exploration of Sparse Tensor Accelerator Designs by Whoi Ree Ha, Hyunjun Kim, Yunheung Paek

Subjects: “…AI accelerators…”
Get full text

Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware by Petro Junior Milan, Hongqian Rong, Craig Michaud, Naoufal Layad, Zhengchun Liu, Ryan Coffee, Ryan Coffee

Subjects: “…AI acceleration…”
Get full text

A 64-TOPS Energy-Efficient Tensor Accelerator in 14nm With Reconfigurable Fetch Network and Processing Fusion for Maximal Data Reuse by Sang Min Lee, Hanjoon Kim, Jeseung Yeon, Juyun Lee, Younggeun Choi, Minho Kim, Changjae Park, Kiseok Jang, Youngsik Kim, Yongseung Kim, Changman Lee, Hyuck Han, Won Eung Kim, Rui Tang, Joon Ho Baek

Subjects: “…AI accelerators…”
Get full text

An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications by Georgios P. Koudouridis, Serveh Shalmashi, Reza Moosavi

Get full text

Security leaders learn AI fundamentals through MIT professional program by Chase, Brittany

“…The AI Accelerator partnered with MIT Lincoln Laboratory, DOD’s Chief Digital and Artificial Intelligence Office, and MIT’s Computer Science and Artificial Intelligence Laboratory to offer the AI for National Security Leaders course, teaching defense officials the basics of modern AI. …”
Get full text

Network Intrusion Detection via Flow-to-Image Conversion and Vision Transformer Classification by Chi Mai Kim Ho, Kin-Choong Yow, Zhongwen Zhu, Sarang Aravamuthan

Get full text

First Commercial Company Leverages DAF-MIT Maneuver ID Challenge to Advance AI

Get full text

Roadmap for Uncertainty: Implementing Modernization and Operationalizing Change by Sneider, Ethan

Get full text

Phantom Fellowship Program Culminates in BAH Optimization Project

Get full text

MagNav Project Successfully Demonstrates Real-Time Magnetic Navigation

Get full text

Live demonstration: man-in-the-middle attack on edge artificial intelligence by Hu, Bowen, He, Weiyang, Wang, Si, Liu, Wenye, Chang, Chip Hong

“…A tiny MIPI FPGA chip with hardened CSI-2 and D-PHY blocks is attached between the camera and the edge AI accelerator to inject unobtrusive stripes onto the RAW image data. …”
Get full text
Get full text

On a quantum inspired approach to train machine learning models by Jean Michel Sellier

Get full text

Open sub-granting radio resources in overlay D2D-based V2V communications by Dariush Moahammad Soleymani, Mohammad Reza Gholami, Giovanni Del Galdo, Jens Mueckenheim, Andreas Mitschele-Thiel

Get full text

UAV-Based and WebRTC-Based Open Universal Framework to Monitor Urban and Industrial Areas by Agnieszka Chodorek, Robert Ryszard Chodorek, Paweł Sitek

“…WebRTC offers simultaneous transmission of both a real-time video stream and the flow of data coming from sensors, and ensures a kind of protection of data flow, which leads to preserving its near-real-time character and enables contextual communication. Addition of the AI accelerator hardware makes this system AI-ready, i.e., the IoT communication hub, which is the air component of our system, is able to perform tasks of AI-supported computing. …”
Get full text

Accelerating AI performance with the incorporation of TVM and MediaTek NeuroPilot by Chao-Lin Lee, Chun-Ping Chung, Sheng-Yuan Cheng, Jenq-Kuen Lee, Robert Lai

“…In this paper, we present an innovative method for integrating TVM's quantisation flow with the MediaTek Neuropilot AI accelerator. We outline the process of converting the TVM relay intermediate-representation quantised neural network dialect model to a tensor-oriented quantisation format, with the aim of harnessing the full potential of both TVM and MediaTek NeuroPilot. …”
Get full text

Divergence-Based Transferability Analysis for Self-Adaptive Smart Grid Intrusion Detection With Transfer Learning by Pengyi Liao, Jun Yan, Jean Michel Sellier, Yongxuan Zhang

Get full text