8f43927575
examples/ is based on KasperskyLab/knp at 55470ed6 with local changes to the mnist-learn altai/blifat models, cuda-backend CMake and model_loader. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
71 lines
2.5 KiB
C++
71 lines
2.5 KiB
C++
// Experiment-only cache for the fixed-topology CPU training backend.
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#pragma once
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#include <knp/core/projection.h>
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#include <cstdint>
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#include <functional>
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#include <map>
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#include <utility>
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#include <vector>
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namespace knp::backends::cpu::populations::impl::training::stdp
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{
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template <class Synapse>
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struct ConnectionCacheEntry
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{
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using Projection = knp::core::Projection<Synapse>;
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using Params = knp::synapse_traits::synapse_parameters<Synapse>;
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std::vector<Projection*> projections;
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std::vector<size_t> sizes;
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std::vector<const void*> storage;
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std::vector<std::reference_wrapper<Params>> values;
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};
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template <class Synapse>
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auto& connection_cache()
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{
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using Key = std::pair<std::uintptr_t, size_t>;
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static thread_local std::map<Key, ConnectionCacheEntry<Synapse>> cache;
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return cache;
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}
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template <class Synapse>
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void clear_connection_cache()
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{
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connection_cache<Synapse>().clear();
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}
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template <class Synapse>
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const std::vector<std::reference_wrapper<knp::synapse_traits::synapse_parameters<Synapse>>>&
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get_all_connected_synapses(
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std::vector<std::reference_wrapper<knp::core::Projection<Synapse>>>& projections, size_t neuron)
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{
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using Projection = knp::core::Projection<Synapse>;
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using Values = std::vector<std::reference_wrapper<knp::synapse_traits::synapse_parameters<Synapse>>>;
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static const Values empty;
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if (projections.empty()) return empty;
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auto key = std::make_pair(reinterpret_cast<std::uintptr_t>(&projections.front().get()), neuron);
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auto& entry = connection_cache<Synapse>()[key];
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bool valid = entry.projections.size() == projections.size();
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for (size_t i = 0; valid && i < projections.size(); ++i)
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{
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auto& p = projections[i].get();
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const void* storage = p.size() ? static_cast<const void*>(&p[0]) : nullptr;
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valid = entry.projections[i] == &p && entry.sizes[i] == p.size() && entry.storage[i] == storage;
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}
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if (!valid)
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{
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entry = ConnectionCacheEntry<Synapse>{};
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for (auto& ref : projections)
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{
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auto& p = ref.get();
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entry.projections.push_back(&p);
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entry.sizes.push_back(p.size());
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entry.storage.push_back(p.size() ? static_cast<const void*>(&p[0]) : nullptr);
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// Preserve the original index order, hence the floating-point update order.
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for (auto index : p.find_synapses(neuron, Projection::Search::by_postsynaptic))
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entry.values.emplace_back(std::get<knp::core::synapse_data>(p[index]));
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}
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}
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return entry.values;
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}
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}
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