Paper
Research
MultiVic: A Time-Predictable RISC-V Multi-Core Processor Optimized for Neural Network Inference
Abstract
MultiVic is a novel, time-predictable RISC-V multi-core vector processor designed to meet the high-performance and strict predictability requirements of neural network inference in real-time systems. The architecture ensures predictable timing behavior by
Research
TT-Edge: A Hardware-Software Co-Design for Energy-Efficient Tensor-Train Decomposition on Edge AI
Abstract
TT-Edge is a hardware-software co-designed framework engineered to overcome the high latency and energy costs associated with Tensor Train Decomposition (TTD) on resource-constrained edge AI devices. It achieves this by offloading compute-intensive
Research
FiCABU: A Fisher-Based, Context-Adaptive Machine Unlearning Processor for Edge AI
Abstract
FiCABU (Fisher-based Context-Adaptive Balanced Unlearning) is a novel software-hardware co-design that integrates efficient machine unlearning capabilities into a RISC-V edge AI processor. This system employs Context-Adaptive Unlearning, starting edits from back-end layers,
Research
FeNN-DMA: A RISC-V SoC for SNN acceleration
Abstract
Spiking Neural Networks (SNNs), despite their energy efficiency, are poorly matched to conventional accelerators due to low arithmetic intensity. To address this, the authors introduce FeNN-DMA, a novel, fully-programmable RISC-V System-on-Chip tailored
Research
DRsam: Detection of Fault-Based Microarchitectural Side-Channel Attacks in RISC-V Using Statistical Preprocessing and Association Rule Mining
Abstract
DRsam is a novel detection method addressing the vulnerability of RISC-V processors to fault-based microarchitectural side-channel attacks, a security area often neglected compared to x86 and ARM. This approach combines statistical preprocessing
Research
SmaRTLy: RTL Optimization with Logic Inferencing and Structural Rebuilding
Abstract
SmaRTLy is a novel RTL optimization technique specifically designed to enhance multiplexer tree synthesis by utilizing advanced logic inferencing and structural rebuilding methods. This approach overcomes the limitations of traditional synthesis tools