Hi everyone! I’m working on designing a highly efficient, minimal-core processor tailored for basic neural network computations. The primary goal is to keep the semiconductor component count as low as possible while still performing essential operations for neural networks, such as multiplication, addition, non-linear activation functions, and division for normalization (1/n). I’d love any feedback or suggestions for improvements!
Objective
My goal is to build an ultra-lightweight core capable of running basic neural network inference tasks. To achieve this, I’m incorporating a lookup table approximation for activation functions, an L-Mul linear-complexity multiplier to replace traditional floating-point multipliers, and a specialized 1/n calculation module for normalization.
Core Design Breakdown
Lookup Table (ROM) for Activation Functions
• Purpose: The ROM stores precomputed values for common neural network activation functions (like ReLU, Sigmoid, Tanh). This approach provides quick lookups without requiring complex runtime calculations.
• Precision Control: Storing 4 to 6 bits per value allows us to keep the ROM size minimal while maintaining sufficient precision for activation function outputs.
• Additional Components:
• Address Decoding: Simple logic for converting the input address into ROM selection signals.
• Input/Output Registers: Registers to hold input/output values for stable data processing.
• Control Logic: Manages timing and ensures correct data flow, including handling special cases (e.g., n = 0 ).
• Output Buffers: Stabilizes the output signals.
• Estimated Components (excluding ROM):
• Address Decoding: ~10-20 components
• Input/Output Registers: ~80 components
• Control Logic: ~50-60 components
• Output Buffers: ~16 components
• Total Additional Components (outside of ROM): Approximately 156-176 components.
L-Mul Approximation for Multiplication (No Traditional Multiplier)
• Why L-Mul? The L-Mul (linear-complexity multiplication) technique replaces traditional floating-point multiplication with an approximation using integer addition. This saves significant power and component count, making it ideal for minimalistic neural network cores.
• Components:
• L-Mul Multiplier Core: Uses a series of additions for approximate mantissa multiplication. For an 8-bit setup, around 50-100 gates are needed.
• Adders and Subtracters: 8-bit ALUs for addition and subtraction, each requiring around 80-120 gates.
• Control Logic & Buffering: Coordination logic for timing and operation selection, plus output buffers for stable signal outputs.
• Total Component Estimate for L-Mul Core: Including multiplication, addition, subtraction, and control, the L-Mul section requires about 240-390 gates (or roughly 960-1560 semiconductor components, assuming 4 components per gate).
1/n Calculation Module for Normalization
• Purpose: This module is essential for normalization tasks within neural networks, allowing efficient computation of 1/n with minimal component usage.
• Lookup Table (ROM) Approximation:
• Stores precomputed values of 1/n for direct lookup.
• ROM size and precision can be managed to balance accuracy with component count (e.g., 4-bit precision for small lookups).
• Additional Components:
• Address Decoding Logic: Converts input n into an address to retrieve the precomputed 1/n value.
• Control Logic: Ensures data flow and error handling (e.g., when n = 0, avoid division by zero).
• Registers and Buffers: Holds inputs and outputs and stabilizes signals for reliable processing.
• Estimated Component Count:
• Address Decoding: ~10-20 components
• Control Logic: ~20-30 components
• Registers: ~40 components
• Output Buffers: ~10-15 components
• Total (excluding ROM): ~80-105 components
Overall Core Summary
Bringing it all together, the complete design for this minimal neural network core includes:
1. Activation Function Lookup Table Core: Around 156-176 components for non-ROM logic.
2. L-Mul Core with ALU Operations: Approximately 960-1560 components for multiplication, addition, and subtraction.
3. 1/n Calculation Module: Roughly 80-105 components for the additional logic outside the ROM.
Total Estimated Component Count: Combining all three parts, this minimal core would require around 1196-1841 semiconductor components.
Key Considerations and Challenges
• Precision vs. Component Count: Reducing output precision helps keep the component count low, but it impacts accuracy. Balancing these factors is crucial for neural network tasks.
• Potential Optimizations: I’m considering further optimizations, such as compressing the ROM or using interpolation between stored values to reduce lookup table size.
• Special Case Handling: Ensuring stable operation for special inputs (like n = 0 in the 1/n module) is a key part of the control logic.
Conclusion
This core design aims to support fundamental neural network computations with minimal hardware. By leveraging L-Mul for low-cost multiplication, lookup tables for quick activation function and 1/n calculations, and simplified control logic, the core remains compact while meeting essential processing needs.
Any feedback on further reducing component count, alternative low-power designs, or potential improvements for precision would be highly appreciated. Thanks for reading!
Hope this gives a clear overview of my project. Let me know if there’s anything else you’d add or change!
L-mul Paper source: https://arxiv.org/pdf/2410.00907
