2 citations · 2 across the 3 of their papers we have counts for
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TADA: Technology Aware DNN Accelerator Performance and Energy Estimator
Tanvi Sharma, Surya Selvam, Anand Raghunathan +1
Deep neural network (DNN) accelerators for edge inference must meet tight energy, latency, and area (EDP) constraints, and their optimal hardware architecture and dataflow configur…
Agentic Design Space Exploration for Joint Hardware Configuration Selection and Mapping of AI Inference Workloads on Heterogeneous Edge SoCs
Geetha Prasuna Yarramneni, Surya Selvam, Wilfried Haensch +1
Modern edge Systems-on-Chip (SoCs) integrate heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, each with distinct performance and energy characteristics. Deploying…
FuSeConv: Fully Separable Convolutions for Fast Inference on Systolic Arrays
Surya Selvam, Vinod Ganesan, Pratyush Kumar
Both efficient neural networks and hardware accelerators are being explored to speed up DNN inference on edge devices. For example, MobileNet uses depthwise separable convolution t…