Livt.ML

src/convolution/QConv2D8x16Kernel5Image14Stream.lvt

namespace Livt.ML.Convolution

using Livt.ML.Numeric

/**
 * Resource-shared quantized SAME convolution for eight 14x14 uint8 planes and
 * sixteen output channels with 5x5 signed-int8 kernels.
 *
 * The interface matches QConv2D1x8Kernel5Image28Stream. One output pixel is
 * evaluated by a parallel eight-channel 5x5 specialization.
 */
component QConv2D8x16Kernel5Image14Stream
{
	// Owned scheduled requantizer; processing holds input acceptance until completion.
	requantize: ScheduledRequantizeUInt8

	// Weight-loading payload and toggle acknowledgment.
	weightRequest: logic
	weightData: logic[8]
	weightAcknowledge: logic

	// Parameter-loading payload and toggle acknowledgment.
	parameterRequest: logic
	parameterBias: int
	parameterMultiplier: int
	parameterDivisor: int
	parameterInputZeroPoint: int
	parameterWeightZeroPoint: int
	parameterOutputZeroPoint: int
	parameterAcknowledge: logic

	// Input payload and toggle acknowledgment from the producer.
	inputRequest: logic
	inputData: byte
	inputLast: logic
	inputAcknowledge: logic

	// Output payload and toggle acknowledgment; payload stays stable while pending.
	outputRequest: logic
	outputData: byte
	outputLast: logic
	outputAcknowledge: logic

	// Configuration readiness and frame-boundary error status.
	configured: logic
	frameError: logic

	// Transfer progress and retained request/acknowledgment state.
	weightSeen: logic
	parameterSeen: logic
	inputSeen: logic
	weightsLoaded: bool
	parametersLoaded: bool
	pending: bool
	frameLoaded: bool

	// Current position and bounds within the configured frame.
	weightChannel: int
	weightIndex: int
	parameterChannel: int
	inputIndex: int
	outputChannel: int
	outputRow: int
	outputColumn: int

	// Input tensor storage; populate the required span before computation.
	inputs: byte[1568]

	// Configured coefficients and numeric scaling parameters.
	weights: logic[16, 200, 8]
	biases: int[16]
	multipliers: int[16]
	divisors: int[16]
	inputZeroPoints: int[16]
	weightZeroPoints: int[16]
	outputZeroPoints: int[16]

	/**
	 * Binds configuration, input, and output toggle-handshake ports. Callers hold each payload
	 * until its acknowledgment matches the request; configure the worker before sending
	 * frames.
	 */
	new(weightRequest: in logic, weightData: in logic[8], weightAcknowledge: out logic,
		parameterRequest: in logic, parameterBias: in int,
		parameterMultiplier: in int, parameterDivisor: in int,
		parameterInputZeroPoint: in int, parameterWeightZeroPoint: in int,
		parameterOutputZeroPoint: in int, parameterAcknowledge: out logic,
		inputRequest: in logic, inputData: in byte, inputLast: in logic,
		inputAcknowledge: out logic, outputRequest: out logic,
		outputData: out byte, outputLast: out logic, outputAcknowledge: in logic,
		configured: out logic, frameError: out logic)
	{
		requantize = new ScheduledRequantizeUInt8()
		this.weightRequest = weightRequest
		this.weightData = weightData
		weightAcknowledge = this.weightAcknowledge
		this.parameterRequest = parameterRequest
		this.parameterBias = parameterBias
		this.parameterMultiplier = parameterMultiplier
		this.parameterDivisor = parameterDivisor
		this.parameterInputZeroPoint = parameterInputZeroPoint
		this.parameterWeightZeroPoint = parameterWeightZeroPoint
		this.parameterOutputZeroPoint = parameterOutputZeroPoint
		parameterAcknowledge = this.parameterAcknowledge
		this.inputRequest = inputRequest
		this.inputData = inputData
		this.inputLast = inputLast
		inputAcknowledge = this.inputAcknowledge
		outputRequest = this.outputRequest
		outputData = this.outputData
		outputLast = this.outputLast
		this.outputAcknowledge = outputAcknowledge
		configured = this.configured
		frameError = this.frameError
	}

	/**
	 * Parallel SAME-padded eight-channel 5x5 accumulator for this shape.
	 */
	public static fn ComputeAccumulator[](
		inputs: in byte[1568], weights: in logic[16, 200, 8],
		bias: int, inputZeroPoint: int, weightZeroPoint: int,
		outputChannel: int, outputRow: int, outputColumn: int) int
	{
		var accumulator = bias

		for (var inputChannel = 0; inputChannel < 8; inputChannel++) {
			for (var kernelRow = 0; kernelRow < 5; kernelRow++) {
				for (var kernelColumn = 0; kernelColumn < 5; kernelColumn++) {
					var inputRow = outputRow + kernelRow - 2
					var inputColumn = outputColumn + kernelColumn - 2

					if (inputRow >= 0 && inputRow < 14 && inputColumn >= 0 && inputColumn < 14) {
						var weightIndex = inputChannel * 25 + kernelRow * 5 + kernelColumn
						accumulator = QConv2DStreamBase.Accumulate(accumulator,
							inputs[inputChannel * 196 + inputRow * 14 + inputColumn],
							weights[outputChannel, weightIndex], inputZeroPoint, weightZeroPoint)
					}
				}
			}
		}

		return accumulator
	}

	/**
	 * Accepts configuration and frame values through the toggle handshakes, evaluates the
	 * configured operator, and holds each output until acknowledged. All connected endpoints
	 * must share compatible clock/reset sequencing.
	 */
	process Main()
	{
		weightAcknowledge = weightSeen
		parameterAcknowledge = parameterSeen
		inputAcknowledge = inputSeen
		configured = 0b0

		if (weightsLoaded && parametersLoaded) {
			configured = 0b1
		}

		if (!frameLoaded && !pending && weightRequest != weightSeen) {
			weights[weightChannel, weightIndex] = weightData
			weightSeen = weightRequest

			if (weightIndex == 199) {
				weightIndex = 0

				if (weightChannel == 15) {
					weightChannel = 0;
					weightsLoaded = true
				} else {
					weightChannel++
				}
			} else {
				weightIndex++
			}
		}

		if (!frameLoaded && !pending && parameterRequest != parameterSeen) {
			biases[parameterChannel] = parameterBias
			multipliers[parameterChannel] = parameterMultiplier
			divisors[parameterChannel] = parameterDivisor
			inputZeroPoints[parameterChannel] = parameterInputZeroPoint
			weightZeroPoints[parameterChannel] = parameterWeightZeroPoint
			outputZeroPoints[parameterChannel] = parameterOutputZeroPoint
			parameterSeen = parameterRequest

			if (parameterChannel == 15) {
				parameterChannel = 0;
				parametersLoaded = true
			} else {
				parameterChannel++
			}
		}

		if (!frameLoaded && weightsLoaded && parametersLoaded && inputRequest != inputSeen) {
			if (inputIndex == 0) {
				frameError = 0b0
			}

			if (inputLast == 0b1 && inputIndex != 1567) {
				frameError = 0b1
			}

			if (inputIndex == 1567 && inputLast != 0b1) {
				frameError = 0b1
			}

			inputs[inputIndex] = inputData
			inputSeen = inputRequest

			if (inputIndex == 1567) {
				inputIndex = 0
				frameLoaded = true
				outputChannel = 0
				outputRow = 0
				outputColumn = 0
			} else {
				inputIndex++
			}
		}

		if (pending) {
			if (outputAcknowledge == outputRequest) {
				pending = false
			}
		} else {
			if (frameLoaded) {
				var candidate = QConv2D8x16Kernel5Image14Stream.ComputeAccumulator(
					inputs, weights, biases[outputChannel], inputZeroPoints[outputChannel],
					weightZeroPoints[outputChannel], outputChannel, outputRow, outputColumn)
				var finished = requantize.ApplyRational(candidate,
					multipliers[outputChannel], divisors[outputChannel],
					outputZeroPoints[outputChannel])
				outputData = finished as byte
				outputLast = 0b0

				if (outputChannel == 15 && outputRow == 13 && outputColumn == 13) {
					outputLast = 0b1
				}

				outputRequest = !outputRequest
				pending = true

				if (outputColumn == 13) {
					outputColumn = 0

					if (outputRow == 13) {
						outputRow = 0

						if (outputChannel == 15) {
							outputChannel = 0;
							frameLoaded = false
						} else {
							outputChannel++
						}
					} else {
						outputRow++
					}
				} else {
					outputColumn++
				}
			}
		}
	}
}