building forwared()

src/IronpenGPU.jl

@@ -10,8 +10,14 @@ files and each file can only depend on the file included before it.
 include("type.jl")
 using .type    # bring model into this module namespace (this module is a parent module)
 
-include("snnUtils.jl")
+include("snnUtil.jl")
-using .snnUtils
+using .snnUtil
+
+include("forward.jl")
+using .forward
+
+include("learn.jl")
+using .learn
 
 include("interface.jl")
 using .interface
@@ -19,7 +25,7 @@ using .interface
 
 #------------------------------------------------------------------------------------------------100
 
-""" 
+""" version 0.0.1
     Todo:
         [*1] knowledgeFn in GPU format
         [] use partial error update for computeNeuron

src/forward.jl

@@ -2,10 +2,68 @@ module forward
 
 # export
 
-# using
+using GeneralUtils
+using ..type, ..snnUtil
 
 #------------------------------------------------------------------------------------------------100
 
+# kfn forward
+function (kfn::kfn_1)(input::AbstractArray)
+    kfn.timeStep .+= 1
+
+    #TODO time step forward
+    if kfn.learningStage == [1]
+        # reset learning params
+    end
+
+    println(">>> input ", size(input))
+    println(">>> z_i_t1 ", size(kfn.z_i_t1)) 
+
+    # pass input_data into input neuron.
+    GeneralUtils.cartesianAssign!(kfn.z_i_t1, input)
+    println(">>> z_i_t1 ", size(kfn.z_i_t1)) 
+    println(">>> lif_recSignal ", size(kfn.lif_recSignal))
+    println(">>> lif_w ", size(kfn.lif_w))
+    println(">>> lif_refractoryActive ", size(kfn.lif_refractoryActive))
+    println(">>> lif_alpha ", size(kfn.lif_alpha))
+    println(">>> lif_vt0 ", size(kfn.lif_vt0))
+    println(">>> lif_vt0 sum ", sum(kfn.lif_vt0))
+
+    # check active/inactive neurons
+    refractoryStatus!(kfn.lif_refractoryCounter, kfn.lif_refractoryActive, kfn.lif_refractoryInactive)
+    refractoryStatus!(kfn.alif_refractoryCounter, kfn.alif_refractoryActive, kfn.alif_refractoryInactive)
+
+    # LIF forward active neurons
+    kfn.lif_recSignal .= GeneralUtils.sumAlongDim3(
+        GeneralUtils.matMul_3Dto4D_batchwise(kfn.z_i_t1, kfn.lif_refractoryActive .* kfn.lif_w))                          
+    kfn.lif_vt1 = (kfn.lif_alpha .* kfn.lif_vt0) .+ kfn.lif_recSignal
+    # for (i, v) in enumerate(kfn.lif_vt1)
+    #     if v < 
+    # LIF forward inactive neurons
+
+
+
+
+
+    # GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.alif_w, resultStorage=kfn.alif_recSignal)
+    
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+    error("debug end    kfn forward")
+end
+
 
 
 

src/snnUtil.jl

@@ -0,0 +1,78 @@
+module snnUtil
+
+export  refractoryStatus!
+
+# using
+
+#------------------------------------------------------------------------------------------------100
+
+function refractoryStatus!(refractoryCounter, refractoryActive, refractoryInactive)
+    d1, d2, d3, d4 = size(refractoryCounter)
+    for j in 1:d4
+        for i in 1:d3
+            if refractoryCounter[1, 1, i, j] > 0    # inactive
+                view(refractoryActive, 1, 1, i, j) .= 0
+                view(refractoryInactive, 1, 1, i, j) .= 1
+            else
+                view(refractoryActive, 1, 1, i, j) .= 1
+                view(refractoryInactive, 1, 1, i, j) .= 0
+            end
+        end
+    end
+end
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+end # module

src/snnUtils.jl

@@ -1,73 +0,0 @@
-module snnUtils
-
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-# using
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-#------------------------------------------------------------------------------------------------100
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-end # module

src/type.jl

@@ -20,16 +20,35 @@ Base.@kwdef mutable struct kfn_1 <: knowledgeFn
     params::Dict = Dict()  # store params of knowledgeFn itself for later use
 
     timeStep::AbstractArray = [0]
-    refractory::Union{AbstractArray, Nothing} = nothing
     learningStage::AbstractArray = [0]  # 0 inference, 1 start, 2 during, 3 end learning
     z_i_t1::Union{AbstractArray, Nothing} = nothing     # 2D activation matrix
     z_i_t0::Union{AbstractArray, Nothing} = nothing
 
+    # LIF
     lif_w::Union{AbstractArray, Nothing} = nothing
     lif_recSignal::Union{AbstractArray, Nothing} = nothing
+    lif_vt0::Union{AbstractArray, Nothing} = nothing
+    lif_vt1::Union{AbstractArray, Nothing} = nothing
+    lif_vth::Union{AbstractArray, Nothing} = nothing
+    lif_zt0::Union{AbstractArray, Nothing} = nothing
+    lif_zt1::Union{AbstractArray, Nothing} = nothing
+    lif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
+    lif_refractoryActive::Union{AbstractArray, Nothing} = nothing
+    lif_refractoryInactive::Union{AbstractArray, Nothing} = nothing
+    lif_alpha::Union{AbstractArray, Nothing} = nothing
+    lif_delta::AbstractFloat = 1.0
+    lif_tau_m::AbstractFloat = 20.0
 
+    # ALIF
     alif_w::Union{AbstractArray, Nothing} = nothing
     alif_recSignal::Union{AbstractArray, Nothing} = nothing
+    alif_zt0::Union{AbstractArray, Nothing} = nothing
+    alif_zt1::Union{AbstractArray, Nothing} = nothing
+    alif_refractoryCounter::Union{AbstractArray, Nothing} = nothing
+    alif_refractoryActive::Union{AbstractArray, Nothing} = nothing
+    alif_refractoryInactive::Union{AbstractArray, Nothing} = nothing
+
+
 end
 
 # outer constructor
@@ -43,20 +62,25 @@ function kfn_1(params::Dict)
     col += kfn.params[:computeNeuron][:lif][:numbers][2]
     col += kfn.params[:computeNeuron][:alif][:numbers][2]
 
-    kfn.z_i_t1 = zeros(row, col, batch) 
+    # activation matrix
     kfn.z_i_t0 = zeros(row, col, batch) 
+    kfn.z_i_t1 = zeros(row, col, batch)
     
     # LIF
     z = kfn.params[:computeNeuron][:lif][:numbers][1] * kfn.params[:computeNeuron][:lif][:numbers][2]
     kfn.lif_w = zeros(row, col, z)    # matrix z-axis represent each neurons
-    kfn.lif_recSignal = zeros(row, col, z, batch)
+    kfn.lif_recSignal = zeros(1, 1, z, batch)
+    kfn.lif_vt0 = zeros(1, 1, z, batch)
+    kfn.lif_vt1 = zeros(1, 1, z, batch)
+    kfn.lif_vth = ones(1, 1, z, batch)
+    kfn.lif_zt0 = zeros(1, 1, z, batch)
+    kfn.lif_zt1 = zeros(1, 1, z, batch)
+    kfn.lif_refractoryCounter = zeros(1, 1, z, batch)
+    kfn.lif_refractoryActive = zeros(1, 1, z, batch)
+    kfn.lif_refractoryInactive = zeros(1, 1, z, batch)
+    kfn.lif_alpha = ones(1, 1, z, batch) .* (exp(-kfn.lif_delta / kfn.lif_tau_m))
 
-    # ALIF
+    # subscription
-    z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
-    kfn.alif_w = zeros(row, col, z)
-    kfn.alif_recSignal = zeros(row, col, z, batch)
-
-    # lif subscription
     row, col, _ = size(kfn.lif_w)   # row*col is synaptic subscribe weight for each neuron in z-axis
     synapticConnectionPercent = kfn.params[:computeNeuron][:lif][:params][:synapticConnectionPercent]
     synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
@@ -67,7 +91,17 @@ function kfn_1(params::Dict)
         end
     end
 
-    # alif subscription
+    # ALIF
+    z = kfn.params[:computeNeuron][:alif][:numbers][1] * kfn.params[:computeNeuron][:alif][:numbers][2]
+    kfn.alif_w = zeros(row, col, z)
+    kfn.alif_recSignal = zeros(1, 1, z, batch)
+    kfn.alif_zt0 = zeros(1, 1, z, batch)
+    kfn.alif_zt1 = zeros(1, 1, z, batch)
+    kfn.alif_refractoryCounter = zeros(1, 1, z, batch)
+    kfn.alif_refractoryActive = zeros(1, 1, z, batch)
+    kfn.alif_refractoryInactive = zeros(1, 1, z, batch)
+
+    # subscription
     row, col, _ = size(kfn.alif_w)   # row*col is synaptic subscribe weight for each neuron in z-axis
     synapticConnectionPercent = kfn.params[:computeNeuron][:alif][:params][:synapticConnectionPercent]
     synapticConnection = Int(floor(row*col * synapticConnectionPercent/100))
@@ -92,53 +126,6 @@ function kfn_1(params::Dict)
     return kfn
 end
 
-# kfn forward
-function (kfn::kfn_1)(input::AbstractArray)
-    kfn.timeStep .+= 1
-
-    # time step forward
-
-
-
-    # row, col = size(input)  # if input is a 2D matrix
-    println(">>> 1 ", size(input))
-    println(">>> 2 ", size(kfn.z_i_t1)) 
-
-    # multiply input with kfn.z_i_t1 may be using cartesian coordinates
-    GeneralUtils.cartesianAssign!(kfn.z_i_t1, input)
-    println(">>> 3 ", sum(kfn.z_i_t1))
-    println(">>> 4 ", size(kfn.lif_recSignal))
-    println(">>> 5 ", size(kfn.lif_w))
-    kfn.lif_recSignal .= GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.lif_w) 
-    kfn.alif_recSignal .= GeneralUtils.batchMatEleMul(kfn.z_i_t1, kfn.alif_w)
-
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-    error("debug end    kfn forward")
-end
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