I am a third-year Ph.D. student at Università della Svizzera italiana (Lugano, Switzerland), under the supervision of Prof. Laura Pozzi. I am curious and interested about everything related to applied Mathematics, and my education and life choices brought me to the fascinating field of Approximate Computing and, in particular, to the design of approximate hardware for embedded systems. My research work has mostly focused on logic simplification for the design of inexact, energy-efficient circuits, and on the identification of generic algorithms to derive error models of faulty hardware. I have also worked on fault-detection in distributed systems and signal processing, mainly during my master thesis at Politecnico di Milano (Italy). More recently, I have also approached the boolean satisfiability problem applied to logic simplification for approximate circuits, as well as a probabilistic approach for error model derivation.
This work aims at deriving an exhaustive circuit error-model, in terms of identification of the influence of each circuit gate (or component) on the final output. The maximum error that can be seen on the circuit primary output if a gate is removed is obtained through a novel partitiong algorithm: by partitioning the original graph representing the circuit into sub-circuits, and by studying each subgraph truth table separately, it is possible to efficiently obtain an accurate estimate of such influence. The novel partitioning algorithm developed exploits the graph topology and monotonicity of subgraphs to improve the result accuracy.
A novel algorithm that aims at synthesising the most efficient circuits given a predefined error threshold, which denotes the maximum tolerated error for that specific application. By considering a circuit netlist, along with the given error threshold, Circuit Carving performs a binary-tree search on the graph nodes to find the maximum subgraph (i.e., subcircuit) that can be eliminated from the circuit without overcoming the abovementioned threshold. Pruning criteria and, in particular, a definition of closure property for subgraphs are exploited to bound the exploration.
This work is the subject of my master thesis and presents a novel algorithm based on change-detection tests applied on signal frequency bands to identify anomalies in the signal spectrum and adapt the sampling frequency accordingly. This approach can be employed to monitor changes in a phenomenon observed with wireless sensors, where energy savings are vital for a sustainable infrastructure. A first version of the algorithm has also been tested on programmable boards by STMicroelectronics, confirming its efficiency and validity when exposed to real sensors measuring temperature variations.
Early stage of a more detailed work aimed at finding error-models of circuits through a design-and-conquer approach.
Supervisor: Prof. Laura Pozzi.
Research area: approximate computing, design of highly efficient inexact hardware.
Host supervisor: Prof. George Constantinides.
Research area: approximate computing, boolean satisfiability for hardware error-modeling.
Thesis: “A spectrum-based adaptive sampling mechanism for energy conservation in Cyber-Physical Systems”. Later published in the IEEE Smartworld 2018 Conference transactions.
Thesis supervisors: Prof. Cesare Alippi, Prof. Manuel Roveri.
Final grade summa cum laude.
Thesis: “Algorithmics: the spirit of computing”.
Final grade summa cum laude.
Double master degree international program T.I.M.E. (Top Industrial Managers for Europe).
Course resposible: Prof. Laura Pozzi
Course responsible: dr. M. Landoni