SearcharxivSearch

arXiv subjects

Filippo Quadri

Publications and source records attributed to Filippo Quadri.

3 recordsLinked to original sources

Enabling Ultra-Low-Power Always-On Feedforward Leakage Suppression Logic Circuits with FDSOI

The growing deployment of real-time applications on wearable and Internet of Things (IoT) edge devices has intensified the need for energy-efficient, high-performance systems that meet stringent timing and energy constraints. Events-driven architectures leverage the sparsity of real-time to further improve system energy efficiency by employing an always-on (AO) domain to monitor inputs and activate a high-performance (HP) domain only when relevant events occur. However, for low-duty-cycle applications, the energy bottleneck shifts toward the AO domain, where leakage power dominates overall consumption. To mitigate this issue, AO circuits are typically implemented using high voltage threshold (HVT) or ultra-high voltage threshold (UHVT) transistors, thereby avoiding sub- and near-threshold operation, which is highly sensitive to process, voltage, and temperature (PVT) variations. In this context, feedforward leakage suppression logic (FLSL) has recently emerged as a promising candidate, offering reduced leakage compared to conventional. However, previous studies report a significant degradation in FLSL leakage performance in technology nodes below 90 nm, primarily due to increased gate and junction leakage currents. FDSOI technology, with its ability to effectively suppress junction leakage, provides an opportunity to overcome this limitation and restore FLSL efficiency in advanced nodes. Therefore, we demonstrate in this work that FLSL implemented in a 22 nm FDSOI technology can significantly reduce the energy consumption of small AO circuits with low-frequency inputs compared to state-of-the-art ultra-low-power CMOS designs. Silicon measurements on an FIR filter and an AES cryptographic core show reduced operating voltage and up to 9.8 x and 1.83 x reductions in leakage power compared to equivalent HVT and UHVT CMOS implementations, respectively.

cs.ET

HEEPidermis: a versatile SoC for BioZ recording

Biological impedance (BioZ) is an information-packed modality that allows for non-invasive monitoring of health and emotional state. Currently, most research involving tissue impedance is based on bulky or fixed-purpose hardware, which limits the scope of research and the possibilities of experiments. In this work, we present HEEPidermis: a System-on-Chip (SoC) which integrates all the blocks needed for tissue impedance measurement, including two 8-bit, arbitrary-signal current DACs, two VCO-based ADCs, and a RISC-V CPU to enable on-chip feature extraction for closed-loop operation. An event-based sub-sampler improves storage and energy efficiency for long-term recording. In addition to the versatile SoC, the digital back-end and behavioral models of the analog front-end are open-source, allowing fast system-level simulations or repurposing. The SoC was taped out on TSMC 65 nm LP process.

physics.ins-det

Overcoming catastrophic forgetting in neural networks

Catastrophic forgetting is the primary challenge that hinders continual learning, which refers to a neural network ability to sequentially learn multiple tasks while retaining previously acquired knowledge. Elastic Weight Consolidation, a regularization-based approach inspired by synaptic consolidation in biological neural systems, has been used to overcome this problem. In this study prior research is replicated and extended by evaluating EWC in supervised learning settings using the PermutedMNIST and RotatedMNIST benchmarks. Through systematic comparisons with L2 regularization and stochastic gradient descent (SGD) without regularization, we analyze how different approaches balance knowledge retention and adaptability. Our results confirm what was shown in previous research, showing that EWC significantly reduces forgetting compared to naive training while slightly compromising learning efficiency on new tasks. Moreover, we investigate the impact of dropout regularization and varying hyperparameters, offering insights into the generalization of EWC across diverse learning scenarios. These results underscore EWC's potential as a viable solution for lifelong learning in neural networks.

cs.LG