tag: deep neural networks

Accelerator Architecture: Fusion-Aware Mapper (MIT)
By Technical Paper Link - 22 Feb, 2026 - Comments: 0

Researchers from MIT published "Fast and Fusiest: An Optimal Fusion-Aware Mapper for Accelerator Modeling and Evaluation." Abstract "The latency and energy of tensor algebra accelerators depend on how data movement and operations are scheduled (i.e., mapped) onto accelerators, so determining the potential of an accelerator architecture requires both a performance model and a mapper to sea... » read more

Double Duty Logic Block Architecture Enabling Concurrent LUT and Adder Chain Usage (Nanyang Technological Univ. et al)
By Technical Paper Link - 02 Oct, 2025 - Comments: 0

A new technical paper titled "Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage" was published by researchers at Nanyang Technological University, Cornell University, Altera, University of Waterloo and University of Toronto. Abstract "Flexibility and customization are key strengths of Field-Programmable Gate Arrays (FPGAs) when compared to other computing devices... » read more

Accelerator Architecture For In-Memory Computation of CNN Inferences Using Racetrack Memory
By Technical Paper Link - 03 Jul, 2025 - Comments: 0

A new technical paper titled "Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems" was published by researchers at National University of Singapore, A*STAR, Chinese Academy of Sciences, and Hong Kong University of Science and Technology. Abstract "Deep neural networks generate and process large volumes of data, posing challe... » read more

Energy-Aware DL: The Interplay Between NN Efficiency And Hardware Constraints (Imperial College London, Cambridge)
By Technical Paper Link - 20 May, 2025 - Comments: 0

A new technical paper titled "Energy-Aware Deep Learning on Resource-Constrained Hardware" was published by researchers at Imperial College London and University of Cambridge. Abstract "The use of deep learning (DL) on Internet of Things (IoT) and mobile devices offers numerous advantages over cloud-based processing. However, such devices face substantial energy constraints to prolong batte... » read more

Hardware Acceleration Approach for KAN Via Algorithm-Hardware Co-Design
By Technical Paper Link - 26 Sep, 2024 - Comments: 2

A new technical paper titled "Hardware Acceleration of Kolmogorov-Arnold Network (KAN) for Lightweight Edge Inference" was published by researchers at Georgia Tech, TSMC and National Tsing Hua University. Abstract "Recently, a novel model named Kolmogorov-Arnold Networks (KAN) has been proposed with the potential to achieve the functionality of traditional deep neural networks (DNNs) using ... » read more

More Efficient Side-Channel Analysis By Applying Two Deep Feature Loss Functions
By Technical Paper Link - 12 Dec, 2023 - Comments: 0

A technical paper titled “Beyond the Last Layer: Deep Feature Loss Functions in Side-channel Analysis” was published by researchers at Nanyang Technological University, Radboud University, and Delft University of Technology. Abstract: "This paper provides a novel perspective on improving the efficiency of side-channel analysis by applying two deep feature loss functions: Soft Nearest Neig... » read more

A Search Framework That Optimizes Hybrid-Device IMC Architectures For DNNs, Using Chiplets
By Technical Paper Link - 08 Aug, 2023 - Comments: 0

A technical paper titled “HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms” was published by researchers at Yale University. Abstract: "Today, there are a plethora of In-Memory Computing (IMC) devices- SRAMs, PCMs & FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC device offers its own pr... » read more

Leveraging Large Language Models (LLMs) To Perform SW-HW Co-Design
By Technical Paper Link - 28 Jun, 2023 - Comments: 0

A technical paper titled “On the Viability of using LLMs for SW/HW Co-Design: An Example in Designing CiM DNN Accelerators” was published by researchers at University of Notre Dame. Abstract: "Deep Neural Networks (DNNs) have demonstrated impressive performance across a wide range of tasks. However, deploying DNNs on edge devices poses significant challenges due to stringent power and com... » read more

Asynchronously Parallel Optimization Method For Sizing Analog Transistors Using Deep Neural Network Learning
By Technical Paper Link - 03 Mar, 2023 - Comments: 0

A new technical paper titled "APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors Using DNN Learning" was published by researchers at UT Austin and Analog Devices. Abstract "Analog circuit sizing is a high-cost process in terms of the manual effort invested and the computation time spent. With rapidly developing technology and high market demand, bringing automated s... » read more

Review of Tools & Techniques for DL Edge Inference
By Technical Paper Link - 16 Feb, 2023 - Comments: 0

A new technical paper titled "Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review" was published in "Proceedings of the IEEE" by researchers at University of Missouri and Texas Tech University. Abstract: Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying thes... » read more

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