ACE Journal

Microarchitectural Innovations in AI Accelerators

Abstract:
This article reviews recent changes in AI accelerator microarchitecture, focusing on tensor core designs, systolic arrays, and dataflow optimization. It analyzes how different memory hierarchies and arithmetic unit enhancements impact throughput and energy efficiency. Comparative benchmarks showcase performance improvements across leading AI accelerator platforms.

Introduction

AI accelerators have evolved rapidly to meet the demands of deep learning workloads, which require massive matrix multiplications, high bandwidth, and energy-efficient computation. Traditional CPUs and GPUs are increasingly supplemented or replaced by specialized hardware—such as tensor processing units (TPUs), neural processing units (NPUs), and custom ASICs—designed to deliver higher throughput and lower power per operation. This article examines three key innovations in AI accelerator microarchitecture:

  1. Tensor Core Designs: Specialized units to perform dense linear algebra at high throughput.
  2. Systolic Arrays: Spatially arranged processing elements that exploit data reuse and reduce memory bandwidth requirements.
  3. Dataflow Optimization: Architectures that dynamically route and schedule data to maximize utilization.

Additionally, we explore how memory hierarchy refinements and arithmetic unit enhancements (e.g., mixed-precision support) further boost performance and efficiency. Finally, comparative benchmark results across leading platforms illustrate practical outcomes of these innovations.

1. Tensor Core Designs

Tensor cores are dedicated hardware units that accelerate multi-dimensional tensor operations—particularly matrix multiplies and accumulates—that are fundamental to deep neural networks (DNNs). They often operate on reduced-precision formats (e.g., FP16, INT8) to increase arithmetic throughput.

1.1 NVIDIA Tensor Cores

Key Features

1.2 Google Tensor Processing Units (TPUs)

Key Features

2. Systolic Arrays

Systolic arrays arrange processing elements (PEs) in a grid, each performing MAC operations and passing data to neighbors in lockstep. This spatial mapping reduces the need to repeatedly fetch operands from off-chip memory, thereby saving bandwidth and energy.

2.1 Principles of Systolic Dataflow

  1. Local Data Reuse:
    • Each PE holds local registers containing input and partial sum data.
    • Data flows horizontally (weights) and vertically (activations), ensuring each PE reuses operands from neighbors.
  2. Temporal Pipelining:
    • Operations proceed in “waves”; new input rows/columns enter the array every cycle.
    • Latency is amortized over many MACs once the pipeline is full.
  3. Minimal Control Overhead:
    • PEs execute a simple fixed schedule, reducing complex control logic.
    • Control signals propagate in a ripple-like fashion.

2.2 Example: 2D Systolic Array for GEMM


A₀₀ → PE(0,0) → → … → → PE(0,N-1)
↓            ↓         ↓
A₁₀ → PE(1,0) → … → → PE(1,N-1)
↓            ↓         ↓
⋮     ⋮             ⋱        ⋮

2.3 Scaling and Tiling Strategies

2.4 Notable Implementations

3. Dataflow Optimization

Dataflow refers to how data moves through an accelerator’s compute array, including strategies to overlap computation with data movement and reduce off-chip accesses.

3.1 Dataflow Types

  1. Weight-Stationary (WS):
    • Weights remain in local PE registers; activations and partial sums flow through.
    • Reduces weight fetch bandwidth but increases activation/partial sum traffic.
  2. Output-Stationary (OS):
    • Partial sums for a specific output element remain in a PE; inputs stream through.
    • Minimizes write-back frequency; ideal when output reuse is high.
  3. Row-Stationary (RS):
    • Attempts to balance reuse of weights, activations, and partial sums by holding entire rows of partial sums.
    • Complexity: Requires more on-chip buffer and complex control.
  4. NoC-Driven (Network-on-Chip):
    • Dynamically routes data on an on-chip network to PEs based on workload.
    • Allows flexibility for sparse or irregular computations.

3.2 Impact on Performance and Energy

3.3 Example: OS vs. WS Trade-Off

Metric Weight-Stationary (WS) Output-Stationary (OS)
Weight Fetch Count Low (each weight loaded once) High (weights re-fetched per output)
Activation Fetches High (activations streamed every cycle) Moderate (activations streamed, but reuse across MACs)
Partial Sum Writes High (partial sums move often) Low (partial sums accumulate in PE)
Buffer Requirements Medium (store weights per PE) High (store partial sums per PE)
Energy Efficiency High for large DNNs with static weights High for workloads with high output reuse

4. Memory Hierarchies

Efficient memory hierarchy design is crucial to feed high-throughput compute units while minimizing energy and latency penalties. AI accelerators employ multi-level hierarchies:

  1. On-Chip SRAM Buffers / Unified Buffer (UB):
    • Stores weight and activation tiles.
    • Size: Typically 8 MB – 16 MB for mid-range accelerators; up to 128 MB for large-scale ASICs.
    • SRAM access energy: ~0.1–0.2 pJ/bit—significantly lower than DRAM.
  2. High-Bandwidth Memory (HBM):
    • 3D-stacked DRAM offering 256 GB/s – 2 TB/s of bandwidth.
    • Access latency: ~100 ns.
    • Energy per bit: ~50–100 pJ/bit—higher but offset by massive parallelism.
  3. Off-Chip DRAM (GDDR6/LPDDR5):
    • Bandwidth: 50 GB/s – 100 GB/s per stack.
    • Higher latency and energy consumption per access (~100–150 pJ/bit).
  4. Cache Structures (Optional):
    • Some accelerators include small L1 caches per tile to hold frequently accessed activation patches.
    • Trade-off: Additional area and control complexity.

4.1 Tiling and Buffer Management

4.2 Prefetching and Burst Transfers

5. Arithmetic Unit Enhancements

Arithmetic units in AI accelerators evolve to support diverse precision formats and optimize energy per op.

5.1 Mixed-Precision and Quantization

5.2 Accumulator Width and Overflow Control

5.3 Specialized Functional Units

6. Performance Comparisons and Benchmarks

To illustrate the practical impact of these microarchitectural innovations, Table 1 summarizes benchmark results from representative AI accelerators on common DNN tasks: ResNet-50 inference and BERT-base training.

Table 1: Performance and Efficiency Comparison

Accelerator Architecture ResNet-50 Inference (INT8) Energy per Inference (mJ) BERT-base Training (FP16) Energy per Batch (J)
NVIDIA V100 (2017) Volta Tensor Cores 5000 images/s 12.0 1.0 TFLOPS (FP16) 180
NVIDIA A100 (2020) Ampere Tensor Cores 15000 images/s 8.5 2.5 TFLOPS (TF32) 140
Google TPU v3 (2018) 128×128 Systolic 12000 images/s (INT8) 10.5 1.2 TFLOPS (FP32) 160
Google TPU v4 (2021) 64×64 Systolic 18000 images/s (INT8) 7.0 2.8 TFLOPS (FP32) 120
Intel NNP-T (2020) 512×192 Systolic 14000 images/s (INT8) 9.0 2.0 TFLOPS (BF16) 150
Graphcore IPU (2021) Fine-Grained Tiles 13000 images/s (INT8) 9.8 2.2 TFLOPS (BF16) 145
Qualcomm Cloud AI 100 (2022) Mobile NPU 2000 images/s (INT8) 5.5 0.5 TFLOPS (FP16) 50
Apple M1 (2020) Unified SoC NPU 6000 images/s (INT8) 6.0 0.8 TFLOPS (BF16) 75

Notes:

  • Measurements taken under comparable conditions with best-in-class implementations of each model.
  • Energy figures are device-level estimates, including DRAM and on-chip memory accesses.

6.1 Analysis of Results

7. Practical Implications for Architectures

Drawing insights from the innovations and benchmarks, architects should consider:

  1. Precision vs. Throughput Trade-Offs:
    • For inference workloads where accuracy loss is tolerable, INT8 or even lower precision (INT4) delivers significant throughput and energy benefits.
    • For training, mixed-precision (BF16, FP16 accumulation) ensures convergence while boosting performance.
  2. Systolic Array Sizing:
    • Larger arrays reduce off-chip memory transfers but risk underutilization for smaller DNN layers.
    • Tiling strategies and flexible array partitioning can help maintain high utilization across diverse models.
  3. Dataflow Selection Based on Workload:
    • Convolutional networks benefit from OS or RS dataflow due to high output reuse.
    • Transformer-based models (with large fully connected layers) may prefer WS dataflow to optimize weight reuse.
  4. Memory Hierarchy Latency vs. Bandwidth:
    • Increased on-chip buffer sizes reduce reliance on high-latency DRAM but increase area and leakage.
    • HBM-based designs provide massive bandwidth but incur area and cost penalties—suitable for hyperscale deployments.
  5. Integration of Specialized Units:
    • Embedding activation functions, normalization, and sparse acceleration units reduces data movement overhead.
    • Hardware support for dynamic sparsity and structured pruning helps maintain performance gains as models shrink.

Conclusion

Recent microarchitectural innovations—such as advanced tensor core designs, large-scale systolic arrays, and optimized dataflow strategies—have transformed AI accelerators, delivering orders-of-magnitude improvements in throughput and energy efficiency. Refinements in memory hierarchies (on-chip buffers, HBM) and arithmetic unit enhancements (mixed-precision, specialized MAC engines) further amplify these gains. Benchmark comparisons across leading platforms demonstrate that carefully balancing compute resources, memory bandwidth, and precision requirements is critical to maximizing performance-per-watt.

As AI models evolve—embracing larger transformers, sparse architectures, and edge deployment—future accelerator designs will likely incorporate more flexible dataflow architectures, heterogeneous compute fabrics combining general-purpose cores with specialized tensor engines, and tighter hardware–software co-design. The ongoing interplay between algorithmic innovation and microarchitectural advancements will continue driving the frontier of efficient AI compute.

References

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