⚡ Neuromorphic Computing

Code Intelligence with Spikes

Spiking neural networks for program analysis and code generation. Faster inference, lower energy, competitive accuracy.

3.2×

Faster Inference

92%

Energy Reduction

64.5%

Accuracy@1

neural_ast_builder.py

class NeuralASTBuilder:
    def encode(self, source):
        # Convert AST to spike trains
        ast = parse(source)
        spikes = self.traverse(ast)
        return spikes

Architecture Pipeline

From source code to spike-based completion

📄

Source Code

Python, JS, etc.

→

🌳

AST Parser

Syntax tree

→

⚡

Spike Encoder

Temporal patterns

→

🧠

SNN Reasoner

Pattern matching

→

✨

Output

Completion/Bug

AST to Spike Encoding

AST traversal maps to temporal spike patterns

Spike Train Visualization

Type encoding

Value encoding

Structure encoding

Experimental Results

Performance on code intelligence benchmarks

Code Completion (Latency)

CodeLlama

520ms

CodeT5

168ms

NCI (Ours)

45ms

Bug Detection F1

DeepBugs

71.4%

CodeBERT

78.3%

NCI (Ours)

76.9%

Energy per Sample

GPU (V100)

12 mJ

Loihi

0.8 mJ

When NCI Excels

Ideal applications for neuromorphic code intelligence

⚡

Real-Time IDE

Sub-50ms latency for instant code completion without lag

📱

Edge Deployment

Run on resource-constrained devices with minimal battery impact

🔍

Clone Detection

Fast structural pattern matching for duplicate code identification

📚

Long Code

8× better scaling with code length than transformer models

Explore the Research

Complete architecture, spike encoding details, and benchmark results.

Read Whitepaper All Research