HMN-384
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Hmn-384 May 2026

At execution time, the H‑Scheduler monitors spike traffic and dynamically migrates workloads to balance power consumption across the mesh. If a hotspot emerges (e.g., a burst of visual events), the scheduler can:

Robotic arms in a factory floor can host an HMN‑384 to perform real‑time force feedback and predictive maintenance. The analog spikes encode tactile events with sub‑microsecond resolution, while the hybrid dense units execute lightweight transformer models that predict component wear, all within a confined thermal envelope suitable for industrial enclosures.

We evaluated the antiproliferative activity of HMN-384 across a panel of breast cancer cell lines. HMN-384 exhibited potent cytotoxicity in TNBC lines (MDA-MB-231, BT-549) with GI50 values ranging from 12 to 28 nM, whereas luminal breast cancer lines (MCF-7, T47D) were significantly less sensitive. HMN-384

Mechanistically, treatment with HMN-384 resulted in:

In the last decade, the demand for intelligent computation has shifted from the cloud to the edge. Autonomous vehicles, wearable health monitors, smart factories, and immersive mixed‑reality systems all require on‑device AI that can operate with low latency, high reliability, and minimal energy consumption. Conventional von‑Neumann processors—whether general‑purpose CPUs, GPUs, or even specialized AI accelerators—are increasingly strained by the memory‑bandwidth wall and the thermal limits of dense silicon. At execution time, the H‑Scheduler monitors spike traffic

Neuromorphic computing, which emulates the event‑driven, highly parallel nature of biological neural networks, promises a dramatic reduction in energy per operation. Yet, early neuromorphic chips (e.g., IBM’s TrueNorth, Intel’s Loihi) have struggled to integrate with mainstream software stacks and to deliver the raw throughput demanded by modern deep‑learning workloads. The HMN‑384 is conceived as a hyper‑modular response to these challenges, marrying a highly configurable analog‑digital hybrid core with a seamless software ecosystem.

The HMN‑384 incorporates multi‑level voltage scaling and event‑driven power gating: At execution time

Combined, these mechanisms enable sub‑watt operation for inference on moderately sized models (e.g., a ResNet‑18 analog equivalent consumes ≈ 0.8 W at 30 fps on a 1080p video stream).

At execution time, the H‑Scheduler monitors spike traffic and dynamically migrates workloads to balance power consumption across the mesh. If a hotspot emerges (e.g., a burst of visual events), the scheduler can:

Robotic arms in a factory floor can host an HMN‑384 to perform real‑time force feedback and predictive maintenance. The analog spikes encode tactile events with sub‑microsecond resolution, while the hybrid dense units execute lightweight transformer models that predict component wear, all within a confined thermal envelope suitable for industrial enclosures.

We evaluated the antiproliferative activity of HMN-384 across a panel of breast cancer cell lines. HMN-384 exhibited potent cytotoxicity in TNBC lines (MDA-MB-231, BT-549) with GI50 values ranging from 12 to 28 nM, whereas luminal breast cancer lines (MCF-7, T47D) were significantly less sensitive.

Mechanistically, treatment with HMN-384 resulted in:

In the last decade, the demand for intelligent computation has shifted from the cloud to the edge. Autonomous vehicles, wearable health monitors, smart factories, and immersive mixed‑reality systems all require on‑device AI that can operate with low latency, high reliability, and minimal energy consumption. Conventional von‑Neumann processors—whether general‑purpose CPUs, GPUs, or even specialized AI accelerators—are increasingly strained by the memory‑bandwidth wall and the thermal limits of dense silicon.

Neuromorphic computing, which emulates the event‑driven, highly parallel nature of biological neural networks, promises a dramatic reduction in energy per operation. Yet, early neuromorphic chips (e.g., IBM’s TrueNorth, Intel’s Loihi) have struggled to integrate with mainstream software stacks and to deliver the raw throughput demanded by modern deep‑learning workloads. The HMN‑384 is conceived as a hyper‑modular response to these challenges, marrying a highly configurable analog‑digital hybrid core with a seamless software ecosystem.

The HMN‑384 incorporates multi‑level voltage scaling and event‑driven power gating:

Combined, these mechanisms enable sub‑watt operation for inference on moderately sized models (e.g., a ResNet‑18 analog equivalent consumes ≈ 0.8 W at 30 fps on a 1080p video stream).