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09/07/2023
Alzheimer Disease Detection from Raman Spectroscopy of the Cerebrospinal Fluid via Topological Machine Learning
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical dia...
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07/06/2023
Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge
Mixed-precision quantization, where a deep neural network's layers are q...
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07/03/2023
A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
The emerging trend of deploying complex algorithms, such as Deep Neural ...
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06/27/2023
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms
Recent trends in deep learning (DL) imposed hardware accelerators as the...
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05/30/2023
Reduced Precision Floating-Point Optimization for Deep Neural Network On-Device Learning on MicroControllers
Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller U...
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05/15/2023
Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30
Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) Sys...
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05/12/2023
Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays
Emerging applications in the IoT domain require ultra-low-power and high...
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03/31/2023
DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training
On-chip DNN inference and training at the Extreme-Edge (TinyML) impose s...
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01/24/2023
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge
Neural Architecture Search (NAS) is quickly becoming the go-to approach ...
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01/10/2023
RedMule: A Mixed-Precision Matrix-Matrix Operation Engine for Flexible and Energy-Efficient On-Chip Linear Algebra and TinyML Training Acceleration
The increasing interest in TinyML, i.e., near-sensor machine learning on...
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11/23/2022
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture
The demand for computation resources and energy efficiency of Convolutio...
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06/03/2022
Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm f...
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04/24/2022
RedMulE: A Compact FP16 Matrix-Multiplication Accelerator for Adaptive Deep Learning on RISC-V-Based Ultra-Low-Power SoCs
The fast proliferation of extreme-edge applications using Deep Learning ...
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04/22/2022
SNE: an Energy-Proportional Digital Accelerator for Sparse Event-Based Convolutions
Event-based sensors are drawing increasing attention due to their high t...
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03/28/2022
Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks
Temporal Convolutional Networks (TCNs) are promising Deep Learning model...
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03/24/2022
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference
Temporal Convolutional Networks (TCNs) are emerging lightweight Deep Lea...
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02/14/2022
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference
Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn...
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01/21/2022
Dustin: A 16-Cores Parallel Ultra-Low-Power Cluster with 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode
Computationally intensive algorithms such as Deep Neural Networks (DNNs)...
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01/04/2022
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
Deployment of modern TinyML tasks on small battery-constrained IoT devic...
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10/20/2021
A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays
In the last few years, research and development on Deep Learning models ...
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10/18/2021
Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode
The Internet-of-Things requires end-nodes with ultra-low-power always-on...
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09/03/2021
End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?
In-Memory Acceleration (IMA) promises major efficiency improvements in d...
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03/19/2021
Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVs
Artificial intelligence-powered pocket-sized air robots have the potenti...
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11/29/2020
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Network on RISC-V based IoT End Nodes
This work introduces lightweight extensions to the RISC-V ISA to boost t...
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11/08/2020
Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors
The main design principles in computer architecture have recently shifte...
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10/08/2020
A Mixed-Precision RISC-V Processor for Extreme-Edge DNN Inference
Low bit-width Quantized Neural Networks (QNNs) enable deployment of comp...
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08/17/2020
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extrem...
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07/22/2020
Memory-Latency-Accuracy Trade-offs for Continual Learning on a RISC-V Extreme-Edge Node
AI-powered edge devices currently lack the ability to adapt their embedd...
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07/17/2020
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node
Binary Neural Networks (BNNs) have been shown to be robust to random bit...
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07/15/2020
Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices
The deployment of Quantized Neural Networks (QNN) on advanced microcontr...
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04/13/2020
Technical Report: NEMO DNN Quantization for Deployment Model
This technical report aims at defining a formal framework for Deep Neura...
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08/29/2019
PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors
We present PULP-NN, an optimized computing library for a parallel ultra-...
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05/10/2019
An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diame...
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02/04/2019
Optimally Scheduling CNN Convolutions for Efficient Memory Access
Embedded inference engines for convolutional networks must be parsimonio...
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07/09/2018
XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference
Binary Neural Networks (BNNs) are promising to deliver accuracy comparab...
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05/04/2018
Ultra Low Power Deep-Learning-powered Autonomous Nano Drones
Flying in dynamic, urban, highly-populated environments represents an op...
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12/04/2017
NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs
Deep convolutional neural networks (CNNs) obtain outstanding results in ...
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11/15/2017
Chipmunk: A Systolically Scalable 0.9 mm^2, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference
Recurrent neural networks (RNNs) are state-of-the-art in voice awareness...
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12/18/2016