SearcharxivSearch

arXiv subjects

Luigi Carro

Publications and source records attributed to Luigi Carro.

5 recordsLinked to original sources

When Code Becomes Abundant: Redefining Software Engineering Around Orchestration and Verification

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

cs.SE

Full-Stack Optimization for CAM-Only DNN Inference

The accuracy of neural networks has greatly improved across various domains over the past years. Their ever-increasing complexity, however, leads to prohibitively high energy demands and latency in von Neumann systems. Several computing-in-memory (CIM) systems have recently been proposed to overcome this, but trade-offs involving accuracy, hardware reliability, and scalability for large models remain a challenge. Additionally, for some CIM designs, the activation movement still requires considerable time and energy. This paper explores the combination of algorithmic optimizations for ternary weight neural networks and associative processors (APs) implemented using racetrack memory (RTM). We propose a novel compilation flow to optimize convolutions on APs by reducing their arithmetic intensity. By leveraging the benefits of RTM-based APs, this approach substantially reduces data transfers within the memory while addressing accuracy, energy efficiency, and reliability concerns. Concretely, our solution improves the energy efficiency of ResNet-18 inference on ImageNet by 7.5x compared to crossbar in-memory accelerators while retaining software accuracy.

cs.AR

Vector In Memory Architecture for simple and high efficiency computing

Data movement is one of the main challenges of contemporary system architectures. Near-Data Processing (NDP) mitigates this issue by moving computation closer to the memory, avoiding excessive data movement. Our proposal, Vector-In-Memory Architecture(VIMA), executes large vector instructions near 3D-stacked memories using vector functional units and uses a small data cache to enable short-term data reuse. It provides an easy programming interface and guarantees precise exceptions. When executing stream-behaved applications using a single core, VIMA offers a speedup of up to 26x over a CPU system baseline with vector operations in a single-core processor while spending 93% less energy.

cs.AR

Experimental Findings on the Sources of Detected Unrecoverable Errors in GPUs

We investigate the sources of Detected Unrecoverable Errors (DUEs) in GPUs exposed to neutron beams. Illegal memory accesses and interface errors are among the more likely sources of DUEs. ECC increases the launch failure events. Our test procedure has shown that ECC can reduce the DUEs caused by Illegal Address access up to 92% for Kepler and 98% for Volta.

cs.DC

Noise Figure Evaluation Using Low Cost BIST

A technique for evaluating noise figure suitable for BIST implementation is described. It is based on a low cost single-bit digitizer, which allows the simultaneous evaluation of noise figure in several test points of the analog circuit. The method is also able to benefit from SoC resources, like memory and processing power. Theoretical background and experimental results are presented in order to demonstrate the feasibility of the approach.

cs.OH