Publications by Flavio Ponzina

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ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can ...

To reduce energy consumption, it is possible to operate embedded systems at sub-nominal conditions (e.g., reduced voltage, limited eDRAM refresh rate) that can introduce bit errors in their memories. These errors can affect the stored values of CNN weights ...

The growing popularity of edgeAI requires novel solutions to support the deployment of compute-intense algorithms in embedded devices. In this article, we advocate for a holistic approach, where application-level transformations are jointly conceived with ...

The growing popularity of edge computing has fostered the development of diverse solutions to support Artificial Intelligence (AI) in energy-constrained devices. Nonetheless, comparatively few efforts have focused on the resiliency exhibited by AI workload ...

By supporting the access of multiple memory words at the same time, Bit-line Computing (BC) architectures allow the parallel execution of bit-wise operations in-memory. At the array periphery, arithmetic operations are then derived with little additional o ...

The ever-increasing computational complexity and energy consumption of today's applications, such as Machine Learning (ML) algorithms, not only strain the capabilities of the underlying hardware but also significantly restrict their wide deployment at the ...

The increasing size of Convolutional Neural Networks (CNNs) and the high computational workload required for inference pose major challenges for their deployment on resource-constrained edge devices. In this paper, we address them by proposing a novel In-M ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Codebook-based optimizations are a class of algorithmic-level transformations able to effectively reduce the computing and memory requirements of Convolutional Neural Networks (CNNs). This approach tightly limits the number of unique weights in each layer, ...

Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing, and storage. However, almost all state-of-the-art systems are still built upon th ...

Energy consumption is a significant obstacle to integrate deep learning into edge devices. Two common techniques to curve it are quantization, which reduces the size of the memories (static energy) and the number of accesses (dynamic energy), and voltage s ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Inferences using Convolutional Neural Networks (CNNs) are resource and energy intensive. Therefore, their execution on highly constrained edge devices demands the careful co-optimization of algorithms and hardware. Addressing this challenge, in this paper ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs) can tolerate arithmetic approximations. Nonetheless, perturbations must be applied judiciously, to constrain their impact on accuracy. This is a challengi ...

Flavio Ponzina

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Hardware-Software co-design Methodologies for Edge AI Optimization

Overflow-free compute memories for edge AI acceleration

E2CNN: Ensembles of Convolutional Neural Networks to Improve Robustness Against Memory Errors in Edge-Computing Devices

A hardware/software co-design vision for deep learning at the edge

Error Resilient In-Memory Computing Architecture for CNN Inference on the Edge

Bit-Line Computing for CNN Accelerators Co-Design in Edge AI Inference

An Energy Efficient Soft SIMD Microarchitecture and Its Application on Quantized CNNs

Running Efficiently CNNs on the Edge Thanks to Hybrid SRAM-RRAM In-Memory Computing

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Overflow-free Compute Memories for Edge AI Acceleration

An Accuracy-Driven Compression Methodology to Derive Efficient Codebook-Based CNNs

An Error-Based Approximation Sensing Circuit for Event-Triggered Low-Power Wearable Sensors

Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

A Flexible In-Memory Computing Architecture for Heterogeneously Quantized CNNs

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Using Algorithmic Transformations and Sensitivity Analysis to Unleash Approximations in CNNs at the Edge