Bit-Serial DNN Accelerator Architecture for Energy Efficiency
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Solution Overview
Problem
Deep Neural Networks (DNNs) face challenges in energy efficiency and computational demands, limiting their performance due to high power consumption and the need for improved architectures.
Innovation Solution
The introduction of a bit serial processing approach for DNN accelerators, which processes neurons bit-serially and uses a dispatcher unit to manage data flow, allowing for increased parallelism and reduced precision processing, thereby enhancing computational throughput and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If bit-parallel processing is used for DNN computations, then computational throughput is maintained, but energy consumption is high and execution time does not scale with precision reduction
Solution Approach 1:
The patent segments the numerical representation into multiple bits and processes them sequentially rather than in parallel. The bit-serial processing unit breaks down each multiplication operation into individual bit-level operations, processing one bit at a time through a series of logical operations (AND, XOR, shift). This segmentation allows the system to achieve energy efficiency by reducing the computational complexity from O(n) parallel operations to O(1) sequential operations per bit, while the overall throughput scales linearly with the number of bits processed.
Solution Approach 2:
The patent changes the processing parameter from bit-parallel to bit-serial, fundamentally altering how computational operations are executed. By transitioning from processing multiple bits simultaneously to processing bits sequentially, the system reduces power consumption while maintaining scalable throughput. The execution time becomes directly proportional to the precision (number of bits), creating a linear scaling relationship that allows flexible trade-offs between precision and execution time.
2Measurement precision
If higher precision is used for neuron representation, then accuracy is improved, but execution time increases and energy consumption increases
Solution Approach 1:
The patent introduces dynamic configurability where the system can adjust the number of bits processed based on the specific computational requirements. The bit-serial processing unit can be configured to process different numbers of bits for different layers or operations, allowing the system to optimize between accuracy and execution time dynamically. This flexibility enables the system to use higher precision where needed for accuracy while using lower precision where sufficient, creating a dynamic balance between these competing requirements.
3Use of energy by moving object
If bit-serial processing is implemented, then energy efficiency and scalability are improved, but device complexity increases due to additional control logic
Solution Approach 1:
The patent merges multiple control functions into a unified control unit that manages the bit-serial processing operations. Instead of having separate control logic for each bit operation, the system consolidates the control of AND operations, XOR operations, shift operations, and accumulation into a single coordinated control mechanism. This merging reduces the overall control complexity while still enabling the sophisticated bit-serial processing required for energy-efficient DNN computations.
4Productivity
If precision is reduced for faster processing, then energy efficiency improves, but accuracy deteriorates
Solution Approach 1:
The patent enables dynamic adjustment of precision levels based on the specific computational requirements of different DNN layers and operations. The system can process fewer bits for layers where lower precision is sufficient while maintaining higher precision for layers where accuracy is critical, creating a dynamic optimization strategy that balances processing speed and accuracy across the entire network rather than applying a fixed precision level uniformly.
Data Source
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AI summary
A system for bit-serial computation in a neural network is described. The system may be embodied on an integrated circuit and include one or more bit-serial tiles for performing bit- serial computations in which each bit-serial tile receives input neurons and synapses, and communicates output neurons. Also included is an activation memory for storing the neurons and a dispatcher and a reducer. The dispatcher reads neurons and synapses from memory and communicates either the neurons or the synapses bit-serially to the one or more bit-serial tiles. The other of the neurons or the synapses are communicated bit-parallelly to the one or more bit- serial tiles, or according to a further embodiment, may also be communicated bit-serially to the one or more bit-serial tiles. The reducer receives the output neurons from the one or more tiles, and communicates the output neurons to the activation memory.