Research

NeuroSoC — Offline Neuromorphic Field-Trauma Diagnostic

Founding an independent venture at the European Innovation Academy to build a rugged, offline, low-power handheld that runs many point-of-care diagnostic tests at once on a neuromorphic processor — so a first responder in a disaster zone can diagnose life threats and guide stabilization without power or connectivity. The device is organized around MARCH (Massive hemorrhage, Airway, Respiration, Circulation, Hypothermia/Head), the sequence responders already use, targeting the leading preventable causes of field-trauma death: hemorrhage (~91% of preventable battlefield deaths), airway, and tension pneumothorax.

Research question: Can milliwatt-scale, event-based (spiking) compute integrate many physiological signals offline — at accuracy comparable to conventional networks — to catch life threats before conventional vitals move?

Key Contributions(Phase-0 software prototype)

• Working Phase-0 prototype: an end-to-end event-driven (spiking) pipeline diagnosing from real clinical signals at accuracy comparable to a conventional network, at far fewer operations — the compute basis for a low-power, fully-offline device.
• Four clinical modalities validated on real open data: ECG arrhythmia (MIT-BIH), myocardial-infarction detection (PTB-XL, ~0.89 AUROC), shockable-rhythm / defibrillate-or-not (VFDB + CUDB, ~0.94 AUROC), and heart-sound normal/abnormal (PhysioNet/CinC 2016).
• Runs on commodity neuromorphic silicon (BrainChip Akida): validated that the float-to-on-chip accuracy drop essentially closes (e.g. ECG 0.928 → 0.926 balanced accuracy in Akida simulation), so the models survive deployment.
• Hemorrhage detection pipeline demonstrated: a physiologically-grounded Compensatory-Reserve model flags compensated ('occult') blood loss before heart rate moves — proven as a pipeline; real-data validation is the next step.

Roadmap & Collaboration

• MARCH roadmap: extend from the validated cardiac and auscultation modalities to Respiration/Airway (lung sounds) and a real-data hemorrhage signal, then build a multimodal fusion head across a MARCH stabilization timeline.
• Seeking investors and mentors to take the Phase-0 prototype toward a field-ready device — introductions in neuromorphic hardware, medical devices, and defense / humanitarian health are especially welcome.

DRAM Processing-in-Memory for Fully Homomorphic Encryption

Designed DRAM-based processing-in-memory architectures that run fully homomorphic encryption directly in memory, cutting latency and energy for large-scale polynomial multiplication (4096-element polynomials across 131,072 ciphertexts).

Key Contributions

• Designed and benchmarked three DRAM data-placement strategies (FIGARO, LISA+FIGARO, LISA+RowCopy), reaching 318× speedup over pure in-memory FIGARO and 6.06× lower latency than processor-centric baselines.
• Cut memory energy 41.5× with RowClone-optimized polynomial-shift strategies that minimize costly inter-bank data movement across a 16-bank hierarchy.
• Enabled concurrent processing of 131,072 ciphertexts by combining column-granularity (FIGARO) and hierarchical row-layout (LISA) in-memory paradigms with minimal row-buffer contention.
• Extended Ramulator 2.0 and CipherMatch simulators with subarray-level access tracking and RowCopy latency models to validate architectural feasibility.

Multimodal Neuromorphic Digit Recognition (NeuTNN)

Extended the NeuTNN architecture to classify visual and auditory digits jointly using only biologically-plausible R-STDP learning (no backpropagation), building an edge-ready system that holds stable multimodal representations under strict biological and hardware constraints.

Key Contributions

• Lifted audio-only accuracy from 21% → 90.26% with a log-mel spike-encoding pipeline (per-bin median thresholding) — no changes to the architecture or learning rule.
• Achieved 100% multimodal accuracy on a 72-segment model via a block-diagonal segment mask enforcing strict modality separation, stable across 2:1 and 1:1 visual-to-audio ratios.
• Sustained 100% accuracy at 9.7% synaptic density (~460K of 4.75M synapses) through systematic pruning analysis, quantifying the efficiency relevant to NCAL's edge hardware targets.
• Proposed a dynamic confidence-weighting mechanism — using winning body-potential magnitude as an inference-time reliability signal — to fix robustness under single-modality degradation, with no retraining or architecture change.

3D CNFET Accelerator — Pin Allocation & PCB Bring-Up

Supported bring-up of a monolithic-3D CNFET accelerator within the lab's 3D Integration program, focused on pin allocation and PCB placement-and-routing for the chip's high-speed external test interface.

Key Contributions

• Refined pin allocation across 220+ functional pins over six VHDCI connectors and chip-on-board routing, preserving signal grouping for dual instruction/data buses with zero routing conflicts on the mapped plan.
• Applied SerDes and differential-pair PCB constraints for GHz+ signaling — 100Ω ±10% impedance, <50ps inter-pair skew, 3× spacing for crosstalk isolation.
• Supported DFT pin planning across scan domains and 15+ redundancy configuration pins to preserve yield and diagnostic coverage across VHDCI channels.
• Applied split-voltage power-distribution and ground-stitching constraints (250µm perimeter via spacing, <5% droop targets) for signal integrity under high-speed switching.

Hardware-Aware Sensor Placement for Autonomous Vehicles

Optimizes which sensor — lidar, radar, camera, or disabled — fills each of five fixed AV chassis slots under a target SoC's ingest-bandwidth budget, balancing detection rate, time-to-detect, and cost.

Key Contributions(Phase 1 of 4 complete)

• Reframed the problem as hardware/software co-design where the "software" is the sensor suite and the binding constraint is ingest bandwidth (not memory bandwidth, which never bound in v1) — making the hardware-aware claim substantive.
• Built a CMA-ES optimizer over a continuous relaxation of the 5-slot assignment, with candidates evaluated in parallel MuJoCo ray-cast simulations honoring per-sensor FOV, range, and latency.
• Defined the objective L = w_acc·detection + w_lat·latency + w_cost·cost with a hard ingest-bandwidth cap and two weight presets (safety_first, efficiency).
• Delivered the Phase 1 evaluation infrastructure: spawn-safe parallel workers, Common Random Numbers for fair within-generation comparison, provenance logging (git SHA, MuJoCo version, host), and declarative RUN_CONFIGS for scenario/platform swaps.

Planned Work & Future Directions

• Phase 2 — Reliability: 30-seed bootstrap confidence intervals; four scenarios (straight, urban-cluttered, highway-speed, adversarial-blind); sensor-noise models for rain (lidar), glare (camera), and multipath ghosts (radar).
• Phase 3 — Ablations & SoC sweep: m_lidar, bandwidth-cap, and w_cost sweeps; a five-platform SoC sweep yielding a "bandwidth needed for a 4-sensor AV stack" figure.
• Phase 4 — Manuscript: replace point estimates with CI-bracketed results and add a hardware-co-design section grounded in the SoC sweep.