FrereKhan

5 ms·

1.
by FrereKhan 2y ago
This paper imports an arbitrarily-chosen aspect of cortical architecture — topological maps of function — and ignores every other aspect of biological neural tissue. The resulting models show lower performance for the same number of paramet
2.
by FrereKhan 5y ago
I agree. The use case needs to justify having state, otherwise the ideal architecture is something like NullHop. Temporal signal processing / vision processing tasks are ideal for SNNs, especially if the inputs can also be sparse.
3.
by FrereKhan 5y ago
Ultra-low-power neuromorphic processors such as DynapSE[1] have been cross-bar free for several years now, making them a perfect fit for sparse networks (both weight- and activity-sparsity). [1] https://arxiv.org/abs/17
4.
by FrereKhan 5y ago
If you bring activation sparsity into the mix, the advantage of SNN processors over GPUs/TPUs becomes more clear. Loss-gradient-based optimisation approaches are great because they give you a tool to include e.g. sparsity regularisatio
5.
by FrereKhan 5y ago
It's not quite correct to say this is only for achieving deep learning. Gradient-based parameter optimisation is still a useful tool, even for small shallow networks that would be ideal for event-based signal processing. Even for small
6.
by FrereKhan 8y ago
aiCTX (ai-ctx.com) | Real-time low-power machine learning | SW Eng., Silicon HW Eng., ML research | Zürich, Switzerland | ONSITE, INTERNS, FULL-TIME http://bit.ly/aiCTX_jobs1 aiCTX is a small (<10 employees) startup base