Ultra-low latency neuromorphic computing for robotics, drones, and autonomous systems. Brain-inspired processing meets industrial-grade reliability.
Four specialized processing units working together for real-time autonomous decisions.
Neuromorphic Spike Processing Unit converts sensor data to spikes. Sub-microsecond latency with weighted fusion.
Autonomous Agent Evaluator scores multi-agent decisions. Parallel evaluation across action space.
Real-time confidence estimation using sigmoid activation. Know when to trust decisions.
STDP-inspired weight updates. Learn from experience without cloud connectivity.
Fleet-wide model averaging without centralized data. Privacy-preserving distributed learning.
100% local processing. No cloud required for real-time decisions. Sync when available.
LiDAR, cameras, IMU
~5μs latency
~4μs latency
~3μs latency
Total pipeline: ~12μs sensor-to-actuator
Where milliseconds matter, neuromorphic wins.
Obstacle avoidance at 100+ km/h. Learn from collisions, adapt to new environments in real-time.
Safety-critical reaction times. Redundant decision layer for emergency maneuvers.
Human-robot collaboration with instant reflexes. Safe co-working at production speeds.
Autonomous operation without ground contact. Radiation-tolerant neuromorphic processing.
Python SDK for rapid prototyping. C++ runtime for production deployment.
from thalosforge import NeuromorphicEdge
# Initialize edge service
service = NeuromorphicEdge()
# Sensor data: [left, center, right]
sensor = np.array([0.9, -0.5, 1.1])
# Get decision in ~12μs
result = service.full_decision_loop(sensor)
print(f"Action: {result['action_name']}")
print(f"Confidence: {result['confidence']:.3f}")
print(f"Latency: {result['total_latency_μs']:.2f} μs")
# Training loop with STDP
for episode in range(100):
obs = env.reset()
while not done:
result = service.full_decision_loop(obs)
obs, reward, done = env.step(result['decision'])
service.learn(reward) # STDP updateWhen cloud latency isn't an option, deploy neuromorphic.