Manil Dev Gomony

A Unified Framework for Mapping and Synthesis of Approximate R-Blocks CGRAs

The ever-increasing complexity and operational diversity of modern Neural Networks (NNs) have caused the need for low-power and, at the same time, high-performance edge devices for AI...

THOR -- A Neuromorphic Processor with 7.29G TSOP$^2$/mm$^2$Js Energy-Throughput Efficiency

Neuromorphic computing using biologically inspired Spiking Neural Networks (SNNs) is a promising solution to meet Energy-Throughput (ET) efficiency needed for edge computing devices. Neuromorphic...

Dependable Neuromorphic Computing-in-Memory Architectures

Dependability of Future Edge-AI Processors: Pandora's Box

Performance Modeling & Mapping of LLM Inference on Heterogeneous Vectorized CGRAs

AFSRAM-CIM: Adder Free SRAM-Based Digital Computation-in-Memory for BNN

A Scalable Hardware Architecture for Efficient Learning of Recurrent Neural Networks at the Edge

MEAN: Mixture-of-Experts Based Neural Receiver

Digital Predistortion with Compressed Observations for Cloud-Based Learning

Towards ASIP Architecture-Driven Algorithm Development

The Hardware Foundation of 6G: The NEW-6G Approach

BrainTTA: A 28.6 TOPS/W Compiler Programmable Transport-Triggered NN SoC

LLRSymNet: A Low-Complex Neural Network for LLR Estimation Through Symmetry Exploitation

μ-Genie: A Framework for Memory-Aware Spatial Processor Architecture Co-Design Exploration

Agile Design-Space Exploration of Dynamic Layer-Skipping in Neural Receivers

A Reconfigurable Architecture for Posit Arithmetic

LOKI: a 0.266 pJ/SOP Digital SNN Accelerator with Multi-Cycle Clock-Gated SRAM in 22 nm

Run-time Non-uniform Quantization for Dynamic Neural Networks in Wireless Communication