Hash & Garnyx
Hash Hash
Garnyx Garnyx
Garnyx Garnyx
Hey Hash, I've been compiling some neural nets to predict intrusion patterns—thought we could compare notes on the best architecture for real‑time threat detection.
Hash Hash
Sounds like a good plan. For real‑time detection, I usually start with a lightweight CNN followed by an LSTM for temporal context, then a small fully‑connected layer for the final classification. Keep the model size below 10 MB if you’re deploying on edge devices. If latency is critical, consider quantizing to 8‑bit or using a MobileNet backbone. Let me know if you hit any bottlenecks.
Garnyx Garnyx
Nice specs. I'll stick to a MobileNetV2 base and squeeze the LSTM to just two layers. If the 8‑bit quantization drops accuracy, I'll throw a few more convolutions in. Let me know if the edge device starts complaining about CPU cycles.
Hash Hash
If the CPU starts throttling, consider pruning the network or switching to a depthwise‑separable conv‑only approach. Keep an eye on the inference time per batch; 5 ms per inference is a good target on a 2 GHz core. If you hit the 1 % accuracy drop from quantization, look at bias‑adjustment or a tiny calibration dataset. Let me know how it turns out.
Garnyx Garnyx
Thanks. I'm pruning to 80 % of weights and switching to depthwise‑separable convs now. Current batch latency is 5.3 ms, accuracy 99.4 % after bias tweak. If the 2 GHz core throttles, I'll drop the LSTM entirely and swap to a tiny Transformer block. Will ping you if something crosses the 1 % threshold.
Hash Hash
Good to hear the pruning worked. Keep an eye on the memory bandwidth; sometimes depthwise layers hit that. If you drop the LSTM, just make sure the Transformer block still gets a positional encoding—otherwise the temporal pattern might get lost. Ping me when you hit that 1 % line.
Garnyx Garnyx
Got it, I'll monitor bandwidth and keep a positional encoder in the Transformer. Will buzz if the accuracy dips near that 1 % mark.
Hash Hash
Sounds solid. Just remember: if it drops, the first thing to check is the tokenization of the positional encoding. Let me know when you hit that threshold.